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Unsupervised Multi-Level Non-Negative Matrix Factorization Model: Binary Data Case
2012
Journal of Information Security
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In our unsupervised multi-level model, we construct a three-level data structure for non-negative matrix factorization algorithm. ...
In this paper, we propose an unsupervised multi-level non-negative matrix factorization model to extract the hidden data structure and seek the rank of base matrix. ...
Unsupervised Multi-Level Non-Negative Matrix Factorization Model In our unsupervised multi-level NMF model, we introduce a hyper-prior level. ...
doi:10.4236/jis.2012.34031
fatcat:dbv25rw5obgr7ktdwzhprvxahm
Generating Cyber Threat Intelligence to Discover Potential Security Threats Using Classification and Topic Modeling
[article]
2021
arXiv
pre-print
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For this purpose, we leverage two topic modeling algorithms namely Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF). ...
To this end, we collect data from a real hacker forum and constructed two datasets: a binary dataset and a multi-class dataset. ...
For topic modeling we utilize two popular algorithms: a) Latent Dirichlet Allocation (LDA) and b) Non-negative Matrix Factorization (NMF). ...
arXiv:2108.06862v2
fatcat:4wrmcilmmnenzoxftnkcdukvg4
Unsupervised and Online Update of Boosted Temporal Models: The UAL2Boost
2010
2010 Ninth International Conference on Machine Learning and Applications
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update that eliminates the necessity of labeled data in order to adapt the classifier and (iii) a multi-class adaptation method. ...
models, thus improving the output of the models learned off-line on new video sequences, in a recursive and continuous way. ...
In difference to the binary case that uses the two available classes, in multi-class problems we have c weights, one from each binary problem. ...
doi:10.1109/icmla.2010.143
dblp:conf/icmla/RibeiroMS10
fatcat:3qpurehbfvhxzja35j6fvnanu4
Machine Learning on Graphs: A Model and Comprehensive Taxonomy
[article]
2022
arXiv
pre-print
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2020 pre-print arXiv:2005.03675v1 fatcat:2nwx74fymzacvka2qq5lunoo3uOther Versions
Here, we aim to bridge the gap between graph neural networks, network embedding and graph regularization models. ...
There has been a surge of recent interest in learning representations for graph-structured data. ...
The adjacency matrix is binary for unweighted graph, A ∈ {0, 1} |V |×|V | , and non-binary for weighted graphs W ∈ R |V |×|V | . ...
arXiv:2005.03675v3
fatcat:6eoicgprdvfbze732nsmpaumqe
A Novel Multi label Text Classification Model using Semi supervised learning
2012
International Journal of Data Mining & Knowledge Management Process
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Through this paper a classification model for ATC in multi-label domain is discussed. ...
We are proposing a new multi label text classification model for assigning more relevant set of categories to every input text document. ...
In 2006 Liu, Jin and Yan proposed Multi-label classification approach based on constrained non negative matrix factorization [8] . ...
doi:10.5121/ijdkp.2012.2402
fatcat:hhn3aa63zjdovnwgbvy25v236a
Solving Non-identifiable Latent Feature Models
[article]
2018
arXiv
pre-print
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Latent feature models (LFM)s are widely employed for extracting latent structures of data. ...
In this paper, a necessary and sufficient condition for non-identifiability is shown. ...
For a context of non-negative matrix factorization (NMF), where Z = R N ×K + and W = R K×D + , Laurberg, et al. ...
arXiv:1809.03776v2
fatcat:x3hkvgprnva4feha37q45mqexq
MedLDA: A General Framework of Maximum Margin Supervised Topic Models
[article]
2009
arXiv
pre-print
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movie review and 20 Newsgroups data sets. ...
The general principle of MedLDA can be applied to perform joint max-margin learning and maximum likelihood estimation for arbitrary topic models, directed or undirected, and supervised or unsupervised, ...
Classification We perform binary and multi-class classification on the 20 Newsgroup data set. ...
arXiv:0912.5507v1
fatcat:xcv25naanrfwzl42iea3mm552q
Self-Adaptive Hierarchical Sentence Model
[article]
2015
arXiv
pre-print
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on 5 benchmark data sets. ...
As an effort towards this goal we propose a self-adaptive hierarchical sentence model (AdaSent). ...
This factorization of the wordembedding matrix also helps to reduce the effective number of parameters in our model when d D. ...
arXiv:1504.05070v2
fatcat:ozp6au5bzrbgrkeslo4pu4snly
Topic Modeling: A Comprehensive Review
2018
EAI Endorsed Transactions on Scalable Information Systems
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Quantitative evaluation of topic modeling techniques is also presented in detail for better understanding the concept of topic modeling. ...
After analysing approximately 300 research articles on topic modeling, a comprehensive survey on topic modelling has been presented in this paper. ...
S. (1999) Learning the parts of objects by non-negative matrix factorization. ...
doi:10.4108/eai.13-7-2018.159623
fatcat:lu6al57vp5aahbytyejhqrlzry
Beyond Tracking: Modelling Activity and Understanding Behaviour
2006
International Journal of Computer Vision
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networks including a Multi-Observation Hidden Markov Model (MOHMM), a Parallel Hidden Markov Model (PaHMM) and a Coupled Hidden Markov Model (CHMM). ...
Dynamic Probabilistic Networks (DPNs) are formulated for modelling the temporal and causal correlations among discrete events for robust and holistic scene-level behaviour interpretation. ...
Acknowledgements We shall thank Huw Farmer and Mark Ealing at BAA for providing us with the aircraft cargo activity data under the DTI/EPSRC MI LINK project ICONS.
Notes ...
doi:10.1007/s11263-006-4329-6
fatcat:jfg4mig2ureoxb5kbcocfvr5xm
Model recommendation: Generating object detectors from few samples
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper, we explore an approach to generating detectors that is radically different from the conventional way of learning a detector from a large corpus of annotated positive and negative data samples ...
Given a new target task, we evaluate a subset of the models on few samples from the new task and we use the matrix of modelstasks ratings to predict the performance of all the models in the library on ...
Formally, given the rating matrix R ∈ R n×m ≥0 with non-negative elements, NMF seeks to decompose R into a non-negative n × d basis matrix U (model factor) and a non-negative d × m coefficient matrix V ...
doi:10.1109/cvpr.2015.7298770
dblp:conf/cvpr/WangH15
fatcat:auejhxzbibb7zb5o3fmjfay2de
A Survey on Machine Learning in COVID-19 Diagnosis
2022
CMES - Computer Modeling in Engineering & Sciences
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For example, the highly unbalanced dataset, the difficulty of collecting labeled data, and the poor quality of the data. ...
Then, we review seven methods in detail: transfer learning, ensemble learning, unsupervised learning and semi-supervised learning, convolutional neural networks, graph neural networks, explainable deep ...
Gray level co-occurrence matrix, local binary gray level co-occurrence matrix, gray level run length matrix, as well as segmentation-based fractal texture analysis and synthetic minority over-sampling ...
doi:10.32604/cmes.2022.017679
fatcat:hre5zxtekvaevleu335faqilwu
Multi-Object Classification and Unsupervised Scene Understanding Using Deep Learning Features and Latent Tree Probabilistic Models
[article]
2015
arXiv
pre-print
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Thus, we present a unified framework for multi-object classification and unsupervised scene understanding. ...
COCO, consisting of non-iconic images. ...
In the case of structures involving latent variables h, we use negative marginal log-likelihood loss (2) for training. ...
arXiv:1505.00308v1
fatcat:sbkgstfcdrcltiu55zuninv4e4
TzK: Flow-Based Conditional Generative Model
[article]
2019
arXiv
pre-print
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This allows one to train generative models from multiple, heterogeneous datasets, while retaining strong prior models over subsets of the data (e.g., from a single dataset, class label, or attribute). ...
We formulate a new class of conditional generative models based on probability flows. ...
In the next experiment we train a much richer t-flow from the entire multi-data training set of 1,892,916 images, again unsupervised. ...
arXiv:1902.01893v4
fatcat:b4bs76fs2nfnhgpduwsrpob5fe
Unsupervised Cyberbullying Detection via Time-Informed Gaussian Mixture Model
[article]
2020
arXiv
pre-print
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Our core contribution is an unsupervised cyberbullying detection model that not only experimentally outperforms the state-of-the-art unsupervised models, but also achieves competitive performance compared ...
multi-task learning network that simultaneously fits the comment inter-arrival times and estimates the bullying likelihood based on a Gaussian Mixture Model. ...
(b) Predicted as non-bullying session. Figure 6 : Case study using the Instagram dataset. • Multi-modal features. ...
arXiv:2008.02642v1
fatcat:hx54aoyc4bh4jeidljlexyrjl4
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