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Efficient Tensor Decomposition with Boolean Factors [article]

Sung-En Chang, Xun Zheng, Ian E.H. Yen, Pradeep Ravikumar, Rose Yu
<span title="2020-11-11">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Tensor decomposition has been extensively used as a tool for exploratory analysis. Motivated by neuroscience applications, we study tensor decomposition with Boolean factors.  ...  We propose Binary Matching Pursuit (BMP), a novel generalization of the matching pursuit strategy to decompose the tensor efficiently. BMP iteratively searches for atoms in a greedy fashion.  ...  Latent variable model with Boolean constraints is also known as latent feature model (LFM) [23] in statistical learning, where each observation is associated with a set of real-valued latent features  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1810.04754v2">arXiv:1810.04754v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/624ckrk3irglxicy6ys4jzegoq">fatcat:624ckrk3irglxicy6ys4jzegoq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201114135130/https://arxiv.org/pdf/1810.04754v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/59/8d/598d4179d0a214635d23b3444c5c48b61c4f737d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1810.04754v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Spectral Learning on Matrices and Tensors

Majid Janzamin, Rong Ge, Jean Kossaifi, Anima Anandkumar
<span title="">2019</span> <i title="Now Publishers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ka2h7lkphrfvjlabybgqbnn2jq" style="color: black;">Foundations and Trends® in Machine Learning</a> </i> &nbsp;
Exploiting these aspects turns out to be fruitful for provable unsupervised learning of a wide range of latent variable models.  ...  More crucially, tensor decomposition can pick up latent effects that are missed by matrix methods, e.g. uniquely identify non-orthogonal components.  ...  Furthermore, latent representations are very useful in feature learning. Raw data is in general very complex and redundant and feature learning is about extracting informative features from raw data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1561/2200000057">doi:10.1561/2200000057</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ps6zp5nbcfdpzdwe57fpc4xkta">fatcat:ps6zp5nbcfdpzdwe57fpc4xkta</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321172529/https://www.nowpublishers.com/article/DownloadSummary/MAL-057" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9a/4a/9a4a1e242cda63137d723f5e8d96e6797f0334fe.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1561/2200000057"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Learning Features of Simple and Complex Cells: A Generative Approach via Multiplicative Interactions

Wentao Huang, Wentao Huang, Zhengping Ji, Garrett Kenyon
<span title="2011-05-09">2011</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gfizkffjyzhple2sfhgk46dk2e" style="color: black;">Nature Precedings</a> </i> &nbsp;
three-order tensor weight parameters and two latent variables.  ...  Goal: •A computational model to learn the feature bases (receptive fields) of simple and complex cells in the primary visual cortex.  ...  Learn feature bases 5.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/npre.2011.5943">doi:10.1038/npre.2011.5943</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tf4kcatxurb7lmiurt4rclanoq">fatcat:tf4kcatxurb7lmiurt4rclanoq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812201201/http://precedings.nature.com/documents/5943/version/1/files/npre20115943-1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/93/76/9376eddff67eec7adcab6e3e078880da5e876992.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/npre.2011.5943"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> nature.com </button> </a>

Learning Features of Simple and Complex Cells: A Generative Approach via Multiplicative Interactions

Wentao Huang, Wentao Huang, Zhengping Ji, Garrett Kenyon
<span title="2011-05-09">2011</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/gfizkffjyzhple2sfhgk46dk2e" style="color: black;">Nature Precedings</a> </i> &nbsp;
three-order tensor weight parameters and two latent variables.  ...  Goal: •A computational model to learn the feature bases (receptive fields) of simple and complex cells in the primary visual cortex.  ...  Learn feature bases 5.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/npre.2011.5943.1">doi:10.1038/npre.2011.5943.1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/v2axlfbhjjfrbpisr7jx64hmbi">fatcat:v2axlfbhjjfrbpisr7jx64hmbi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170812201201/http://precedings.nature.com/documents/5943/version/1/files/npre20115943-1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/93/76/9376eddff67eec7adcab6e3e078880da5e876992.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/npre.2011.5943.1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> nature.com </button> </a>

Tensor Factorization for Multi-relational Learning [chapter]

Maximilian Nickel, Volker Tresp
<span title="">2013</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Tensor factorization has emerged as a promising approach for solving relational learning tasks. Here we review recent results on a particular tensor factorization approach, i.e.  ...  Rescal, which has demonstrated state-of-the-art relational learning results, while scaling to knowledge bases with millions of entities and billions of known facts.  ...  A distinctive feature of Rescal is the unique representation of entities via the latent space A.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-40994-3_40">doi:10.1007/978-3-642-40994-3_40</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/p2mpw32u3bcxxelktqeciasyjy">fatcat:p2mpw32u3bcxxelktqeciasyjy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190505015822/https://link.springer.com/content/pdf/10.1007%2F978-3-642-40994-3_40.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2d/e0/2de0f4ac2614069bcfa3bbb6e5ff3b33bfbc3b5f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-40994-3_40"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Explainable Recommendation via Multi-Task Learning in Opinionated Text Data

Nan Wang, Hongning Wang, Yiling Jia, Yue Yin
<span title="">2018</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/ibcfmixrofb3piydwg5wvir3t4" style="color: black;">The 41st International ACM SIGIR Conference on Research &amp; Development in Information Retrieval - SIGIR &#39;18</a> </i> &nbsp;
Two companion learning tasks of user preference modeling for recommendation and opinionated content modeling for explanation are integrated via a joint tensor factorization.  ...  In this work, we develop a multi-task learning solution for explainable recommendation.  ...  Multi-task Learning via a Joint Tensor Factorization Both of our proposed learning tasks are modeled as a tensor factorization problem, and they are coupled with the shared latent factors.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3209978.3210010">doi:10.1145/3209978.3210010</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/sigir/WangWJY18.html">dblp:conf/sigir/WangWJY18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mt27zjgkxzhsvodcgq36f4ncr4">fatcat:mt27zjgkxzhsvodcgq36f4ncr4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200907162544/https://arxiv.org/pdf/1806.03568v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/62/5d/625dc44488dfdbb09ea3bb5a102326769e996f67.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3209978.3210010"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>

Online Continuous-Time Tensor Factorization Based on Pairwise Interactive Point Processes

Hongteng Xu, Dixin Luo, Lawrence Carin
<span title="">2018</span> <i title="International Joint Conferences on Artificial Intelligence Organization"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vfwwmrihanevtjbbkti2kc3nke" style="color: black;">Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence</a> </i> &nbsp;
Each tensor data element has an associated time of occurence and a feature vector.  ...  Compared to existing state-of-the-art methods, our approach captures the latent structure of the tensor and its evolution over time, obtaining superior results on real-world datasets.  ...  Tensor factorization (TF) provides a flexible and effective way to learn such latent structure, decomposing the data into latent factors.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/403">doi:10.24963/ijcai.2018/403</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/ijcai/XuLC18.html">dblp:conf/ijcai/XuLC18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/3mrtbkczi5ejnchaxkk2vcwsdi">fatcat:3mrtbkczi5ejnchaxkk2vcwsdi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430122318/https://www.ijcai.org/proceedings/2018/0403.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/8f/94/8f947dff3987582a60f0512ffea4905310662378.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.24963/ijcai.2018/403"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Multi-resolution Tensor Learning for Large-Scale Spatial Data [article]

Stephan Zheng, Rose Yu, Yisong Yue
<span title="2018-02-28">2018</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
High-dimensional tensor models are notoriously computationally expensive to train. We present a meta-learning algorithm, MMT, that can significantly speed up the process for spatial tensor models.  ...  latent factors), 2) it is orders of magnitude faster than fixed resolution learning and scales to very fine-grained spatial resolutions, and 3) it reliably yields accurate and interpretable models.  ...  In general, given some input x and feature Figure 2 : Prediction process using tensor latent factor model, from full-rank tensor to latent factor model. transformation functions ϕ(·) and ψ (·), tensor  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1802.06825v2">arXiv:1802.06825v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/npigyaxzqvayrcsyyvqweoxt2i">fatcat:npigyaxzqvayrcsyyvqweoxt2i</a> </span>
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Learning General Latent-Variable Graphical Models with Predictive Belief Propagation [article]

Borui Wang, Geoffrey Gordon
<span title="2019-11-28">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Learning general latent-variable probabilistic graphical models is a key theoretical challenge in machine learning and artificial intelligence.  ...  Our proposed algorithm reduces the hard parameter learning problem into a sequence of supervised learning problems, and unifies the learning of different kinds of latent graphical models into a single  ...  Output the learned parameter tensor W S .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1712.02046v2">arXiv:1712.02046v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tnblyvngdzcl7f4castuwesfda">fatcat:tnblyvngdzcl7f4castuwesfda</a> </span>
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Learning General Latent-Variable Graphical Models with Predictive Belief Propagation

Borui Wang, Geoffrey Gordon
<span title="2020-04-03">2020</span> <i title="Association for the Advancement of Artificial Intelligence (AAAI)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wtjcymhabjantmdtuptkk62mlq" style="color: black;">PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE</a> </i> &nbsp;
Learning general latent-variable probabilistic graphical models is a key theoretical challenge in machine learning and artificial intelligence.  ...  Our proposed algorithm reduces the hard parameter learning problem into a sequence of supervised learning problems, and unifies the learning of different kinds of latent graphical models into a single  ...  Output the learned parameter tensor W S .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i04.6076">doi:10.1609/aaai.v34i04.6076</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/csthsqzkc5bipjpq5xyncb7vvi">fatcat:csthsqzkc5bipjpq5xyncb7vvi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20201104111333/https://aaai.org/ojs/index.php/AAAI/article/download/6076/5932" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/0a/77/0a778976fdecc1c517676e2a0a1b9b9e15eaf26b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1609/aaai.v34i04.6076"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Multiresolution Tensor Learning for Efficient and Interpretable Spatial Analysis [article]

Jung Yeon Park, Kenneth Theo Carr, Stephan Zheng, Yisong Yue, Rose Yu
<span title="2020-08-14">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We develop a novel Multiresolution Tensor Learning (MRTL) algorithm for efficiently learning interpretable spatial patterns.  ...  Tensor latent factor models can describe higher-order correlations for spatial data.  ...  Tensor learning parameterizes the model with a weight tensor W (r) ∈ R I×F ×Dr over all features, where I is number of outputs and F is number of non-spatial features.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.05578v5">arXiv:2002.05578v5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5t2godnmznfa3bctwflzy5slve">fatcat:5t2godnmznfa3bctwflzy5slve</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200824203355/https://arxiv.org/pdf/2002.05578v5.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f9/34/f934a3ebc7afb6e193d8c751fc49364a5d70f3d6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.05578v5" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Non-Local Feature Aggregation on Graphs via Latent Fixed Data Structures [article]

Mostafa Rahmani, Rasoul Shafipour, Ping Li
<span title="2021-08-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The locally extracted feature vectors are sorted/distributed on the LFDS and a latent neural network (CNN/GNN) is utilized to perform feature aggregation on the LFDS.  ...  In this paper, we present a novel approach for global feature aggregation in Graph Neural Networks (GNNs) which utilizes a Latent Fixed Data Structure (LFDS) to aggregate the extracted feature vectors.  ...  The tensor/matrix W is learned during the training process along with other parameters of the neural network.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.07028v1">arXiv:2108.07028v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/e5kdix3rrzec3jpjlf4oydwuxq">fatcat:e5kdix3rrzec3jpjlf4oydwuxq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210820184301/https://arxiv.org/pdf/2108.07028v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/9a/ab/9aab1411df4c5d234ad08781924006e7da04f5cc.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.07028v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Low-Dimensional Subject Representation-based Transfer Learning in EEG Decoding

Po-Yuan Jeng, Chun-Shu Wei, Tzyy-Ping Jung, Li-Chun Wang
<span title="2020-09-22">2020</span> <i title="Institute of Electrical and Electronics Engineers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q2z26obphvchndqieqd65vltle" style="color: black;">IEEE journal of biomedical and health informatics</a> </i> &nbsp;
Tensor decomposition was applied to the pre-trial EEG of subjects to extract the underlying characteristics in subject, spatial and spectral domains.  ...  This study presents a transfer-learning framework for EEG decoding based on the low-dimensional representations of subjects learned from the pre-trial EEG.  ...  This type of approach is called the feature-representation-learning in the field of transfer learning.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/jbhi.2020.3025865">doi:10.1109/jbhi.2020.3025865</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/32960770">pmid:32960770</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/653z7bsmabbj5kbl4qjh5h6xgy">fatcat:653z7bsmabbj5kbl4qjh5h6xgy</a> </span>
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Probabilistic Latent Tensor Factorization Model for Link Pattern Prediction in Multi-relational Networks [article]

Sheng Gao and Ludovic Denoyer and Patrick Gallinari
<span title="2012-04-11">2012</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
To address the issue, we propose a Probabilistic Latent Tensor Factorization (PLTF) model by introducing another latent factor for multiple relation types and furnish the Hierarchical Bayesian treatment  ...  To learn the proposed model we develop an efficient Markov Chain Monte Carlo sampling method.  ...  This is a nonparametric latent feature relational model which infers a global set of latent binary features for each object as well as how those latent features interact in the multi-relational networks  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1204.2588v1">arXiv:1204.2588v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/5utozeoaabetlbhjntdnd6zp4a">fatcat:5utozeoaabetlbhjntdnd6zp4a</a> </span>
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Learning from Multi-View Multi-Way Data via Structural Factorization Machines

Chun-Ta Lu, Lifang He, Hao Ding, Bokai Cao, Philip S. Yu
<span title="">2018</span> <i title="ACM Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/s4hirppq3jalbopssw22crbwwa" style="color: black;">Proceedings of the 2018 World Wide Web Conference on World Wide Web - WWW &#39;18</a> </i> &nbsp;
Specifically, we propose structural factorization machines (SFMs) that learn the common latent spaces shared by multi-view tensors and automatically adjust the importance of each view in the predictive  ...  In this paper, we introduce a multi-tensor-based approach that can preserve the underlying structure of multi-view data in a generic predictive model.  ...  factors of each feature are learned.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3178876.3186071">doi:10.1145/3178876.3186071</a> <a target="_blank" rel="external noopener" href="https://dblp.org/rec/conf/www/Lu0DCY18.html">dblp:conf/www/Lu0DCY18</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qtwuk4kjsfdyzlojs35o357s5y">fatcat:qtwuk4kjsfdyzlojs35o357s5y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191015195617/https://arxiv.org/pdf/1704.03037v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/41/90/419090cbd9f89334fa47cd35999e70550a4c2200.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1145/3178876.3186071"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> acm.org </button> </a>
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