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Deep Embedded Complementary and Interactive Information for Multi-View Classification
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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In this work, we propose a novel multi-view learning framework that seamlessly embeds various view-specific information and deep interactive information and introduces a novel multi-view fusion strategy ...
information, deep interactive information between different views, and the strategy of fusing various views. ...
CX201814), State Key Laboratory of Geo-Information Engineering (No. SKLGIE2017-Z-3-2), National NSF of China (Nos. 61432008, 61806092), and Jiangsu Natural Science Foundation (No. BK20180326). ...
doi:10.1609/aaai.v34i04.6122
fatcat:3drk4ngdjbdnldaw7n4ikb2xse
Embedded Deep Bilinear Interactive Information and Selective Fusion for Multi-view Learning
[article]
2020
arXiv
pre-print
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In particular, we train different deep neural networks to learn various intra-view representations, and then dynamically learn multi-dimension bilinear interactive information from different bilinear similarities ...
That is, we seamlessly embed various intra-view information, cross-view multi-dimension bilinear interactive information, and a new view ensemble mechanism into a unified framework to make a decision via ...
It is worth mentioning that we have developed a preliminary work [34] named deep embedded complementary and interactive information for multi-view classification (denoted as MvNNcor). ...
arXiv:2007.06143v1
fatcat:hvq66vutmrfprdv6wp2tpsiwm4
Structure Fusion Based on Graph Convolutional Networks for Node Classification in Citation Networks
2020
Electronics
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By solving this function, we can simultaneously obtain the fusion spectral embedding from the multi-view data and the fusion structure as the adjacent matrix to input graph convolutional networks for node ...
for node classification in citation networks and usually ignore capturing the complete graph structure of nodes for enhancing classification performance. ...
Acknowledgments: The authors would like to thank the anonymous reviewers for their insightful comments that helped improve the quality of this paper. ...
doi:10.3390/electronics9030432
fatcat:tuaxxlo3ibdlpo4bmcaruxg64m
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding
[article]
2019
arXiv
pre-print
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There is an urgent need for methods to obtain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. ...
Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the information from the individual network views ...
The content is solely the responsibility of the authors and does not necessarily represent the official views of the sponsors. ...
arXiv:1909.01084v1
fatcat:hqvq4g5wxvgf7mdq23mc3y4vwa
Survey on Deep Multi-modal Data Analytics: Collaboration, Rivalry and Fusion
[article]
2020
arXiv
pre-print
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With the development of web technology, multi-modal or multi-view data has surged as a major stream for big data, where each modal/view encodes individual property of data objects. ...
Throughout this survey, we further indicate that the critical components for this field go to collaboration, adversarial competition and fusion over multi-modal spaces. ...
Furthermore, it jointly considered multi-view complementary information and class distribution, which enforced all views to learn from each other and enhanced the classification performance. ...
arXiv:2006.08159v1
fatcat:g4467zmutndglmy35n3eyfwxku
Integrate multi-omics data with biological interaction networks using Multi-view Factorization AutoEncoder (MAE)
2019
BMC Genomics
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Our method learns feature and patient embeddings simultaneously with deep representation learning. ...
We developed a method called Multi-view Factorization AutoEncoder (MAE) with network constraints that can seamlessly integrate multi-omics data and domain knowledge such as molecular interaction networks ...
Acknowledgements We thank The Cancer Genome Atlas (TCGA) network for making the high-quality multi-omics data freely available to the public. ...
doi:10.1186/s12864-019-6285-x
pmid:31856727
pmcid:PMC6923820
fatcat:lmx32nlkwngvzgmv4tawn2yzei
A Survey of Multi-View Representation Learning
[article]
2017
arXiv
pre-print
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This paper introduces two categories for multi-view representation learning: multi-view representation alignment and multi-view representation fusion. ...
sparse coding, and multi-view latent space Markov networks, to neural network-based methods including multi-modal autoencoders, multi-view convolutional neural networks, and multi-modal recurrent neural ...
This approach learns multi-modal embeddings for language and visual data and then exploits their complementary information to predict a variable-sized text given an image. ...
arXiv:1610.01206v4
fatcat:xsi7ufxnlbdk5lz6ykrsnexfvm
Recent Deep Learning Methodology Development for RNA–RNA Interaction Prediction
2022
Symmetry
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overview of deep learning models in the prediction of microRNA (miRNA)–mRNA interactions and long non-coding RNA (lncRNA)–miRNA interactions. ...
In recent years, with more and more experimentally verified RNA–RNA interactions being deposited into databases, statistical machine learning, especially recent deep-learning-based automatic algorithms ...
For instance, MVMTMDA [96] infers microRNA-disease associations (MDA) from lncRNA-microRNA interactions by multi-view, multi-task learning. ...
doi:10.3390/sym14071302
fatcat:5lovtdwn3zb4dfbnjitwjnj3y4
A review of heterogeneous data mining for brain disorder identification
2015
Brain Informatics
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disease mechanisms and for informing therapeutic interventions. ...
It is expected that integrating complementary information in the tensor data and the brain network data, and incorporating other clinical parameters will be potentially transformative for investigating ...
Acknowledgments This work is supported in part by NSF through grants III-1526499, CNS-1115234, and OISE-1129076, and Google Research Award. ...
doi:10.1007/s40708-015-0021-3
pmid:27747561
pmcid:PMC4883173
fatcat:rhvqh4vmeffnnoxts7esxwxlsq
Deep learning based feature-level integration of multi-omics data for breast cancer patients survival analysis
2020
BMC Medical Informatics and Decision Making
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Methods Motivated by multi-view learning, we propose a novel strategy to integrate multi-omics data for breast cancer survival prediction by applying complementary and consensus principles. ...
The proposed ConcatAE and CrossAE models can inspire future deep representation-based multi-omics integration techniques. ...
Hang Wu for his kind suggestions on the experiment design and analysis. ...
doi:10.1186/s12911-020-01225-8
pmid:32933515
fatcat:fg25bjnhbvdnhda7t2csotlj5i
Multi-view Factorization AutoEncoder with Network Constraints for Multi-omic Integrative Analysis
[article]
2018
arXiv
pre-print
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Our framework employs deep representation learning to learn feature embeddings and patient embeddings simultaneously, enabling us to integrate feature interaction network and patient view similarity network ...
Here we propose a framework termed Multi-view Factorization AutoEncoder with network constraints to integrate multi-omic data with domain knowledge (biological interactions networks). ...
Each view has a different feature set (for example, gene features, miRNA features, protein features etc.) and can provide complementary information for other views. ...
arXiv:1809.01772v1
fatcat:2ca7u6dt2vet5kdfxri53hreui
Deep Multi-View Learning for Tire Recommendation
[article]
2022
arXiv
pre-print
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2021
2021 International Joint Conference on Neural Networks (IJCNN)
unpublished
doi:10.1109/ijcnn52387.2021.9534318 fatcat:uw52visnxfa75ki37mh4eus5h4
doi:10.1109/ijcnn52387.2021.9534318 fatcat:uw52visnxfa75ki37mh4eus5h4
Our goal is to use a multi-view learning approach to improve our recommender system and improve its capacity to manage multi-view data. ...
The data representing the users, their interactions with the system or the products may come from different sources and be of a various nature. ...
By using several views, all of which provide useful information, a deep composite model is able to naturally make use of the complementary principle. ...
arXiv:2203.12451v1
fatcat:bulihckhxnbjxhos4jtx62l7ym
MuVAM: A Multi-View Attention-based Model for Medical Visual Question Answering
[article]
2021
arXiv
pre-print
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It consists of classification loss and image-question complementary (IQC) loss. ...
Multi-view attention can correlate the question with image and word in order to better analyze the question and get an accurate answer. ...
It consists of classification loss and image-question complementary (IQC) loss. ...
arXiv:2107.03216v1
fatcat:4yqcxspwjrdphl6x22naqvh2aq
Man is What He Eats: A Research on Hinglish Sentiments of YouTube Cookery Channels Using Deep learning
2019
International journal of recent technology and engineering
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Our study focuses on the sentiment analysis of Hinglish comments by multi-label text classification on cookery channels of YouTube using Deep learning. ...
embeddings and customizedembeddings. ...
multi label text classification. ...
doi:10.35940/ijrte.b1153.0982s1119
fatcat:jpdo5d3b7rhzziboqq6ggszxfe
IF-ConvTransformer
2022
Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies
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Then, the extracted features are fed into the applied ConvTransformer subnet for classification. ...
Inspired by the complementary filter, our IMU fusion block performs multi-modal fusion of commonly used sensors according to their physical relationships. ...
In [53] , a hybrid model (DeepSense) composed of a multi-branch CNN and GRUs was proposed for classification. ...
doi:10.1145/3534584
fatcat:isxnkuwfnjbjrnjjkpsvtcydh4
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