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Multimodal Soft Nonnegative Matrix Co-Factorization for Convolutive Source Separation
2017
IEEE Transactions on Signal Processing
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this paper, the problem of convolutive source separation via multimodal soft Nonnegative Matrix Co-Factorization (NMCF) is addressed. ...
Index Terms-multimodality, blind source separation, nonnegative matrix co-factorization, convolutive mixture, audio-visual speech separation.
I. ...
For 35 years, his research interests have been machine learning and source separation, including theory (separability, source separation in nonlinear mixtures, sparsity, multimodality) and applications ...
doi:10.1109/tsp.2017.2679692
fatcat:yqgfce3c35fhdhwdc5t4kvurau
Multiview Approaches to Event Detection and Scene Analysis
[chapter]
2017
Computational Analysis of Sound Scenes and Events
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The interested reader is referred to [139] for more details on the algorithms. 4 The soft co-factorization scheme has proven effective for multichannel [139] and multimodal audio source separation ...
This motivates the soft co-factorization model of Seichepine et al. ...
doi:10.1007/978-3-319-63450-0_9
fatcat:3s3dchsicbg4pkuqrsnrvtkrs4
Multimodal Data Fusion: An Overview of Methods, Challenges, and Prospects
2015
Proceedings of the IEEE
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for diversity across the datasets. ...
We use the term "modality" for each such acquisition framework. ...
David, Inbar Fijalkow, Hagit Messer, Gadi Miller, and Sabine Van Huffel, whose expertise, insightful remarks and feedback have greatly helped extend the scope of this paper; and the anonymous reviewers, for ...
doi:10.1109/jproc.2015.2460697
fatcat:ve7t3be66zfnnahgb7xrlt2lri
Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods
2022
Mathematics
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areas, and the existing review research of hypergraphs; secondly, introduced the theory of hypergraphs briefly; then, compared the learning methods of ordinary graphs and hypergraphs from three aspects: matrix ...
decomposition, random walk, and deep learning; next, introduced the structural optimization of hypergraphs from three perspectives: dynamic hypergraphs, hyperedge weight optimization, and multimodal hypergraph ...
In this paper, multimodal data sources were summarized as both multimodal data and multimodal features. ...
doi:10.3390/math10111921
fatcat:rg75472l5vetph7lfcfrys3jty
Deep Learning in Single-Cell Analysis
[article]
2022
arXiv
pre-print
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This survey will serve as a reference for biologists and computer scientists, encouraging collaborations. ...
We present an overview of the single-cell analytic pipeline pursued in research applications while noting divergences due to data sources or specific applications. ...
Based on non-negative matrix factorization (NMF), scRNA [236] factorizes the source dataset into a gene-independent target data matrix and a data size independent dictionary, derives a new expression ...
arXiv:2210.12385v2
fatcat:ucxfhtrt25azde5jbecoozx2y4
Implementation of Short Video Click-Through Rate Estimation Model Based on Cross-Media Collaborative Filtering Neural Network
2022
Computational Intelligence and Neuroscience
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The experimental results show that the model incorporating multimodal elements improves AUC performance metrics compared to those without multimodal features. ...
The models commonly used in recent years are selected for comparison, and the experimental results show that the proposed model improves in AUC, accuracy, and log loss metrics. ...
a track composed of implicit factors, an association relationship processing model between implicit factor dimensions (only convolutional network models are considered in SECk NCF), and a target prediction ...
doi:10.1155/2022/4951912
pmid:35685157
pmcid:PMC9173947
fatcat:tq3tdoj5qzha3ltxvpaz7thge4
Incorporating User's Preference into Attributed Graph Clustering
2020
IEEE Transactions on Knowledge and Data Engineering
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2020 pre-print arXiv:2003.11079v1 fatcat:b2addg7ti5fyxkhjar7vh4xzre
To this end, we propose two quality measures for a local cluster: Graph Unimodality (GU) and Attribute Unimodality (AU). ...
In contrast to global clustering, local clustering aims to find only one cluster that is concentrating on the given seed vertex (and also on the designated attributes for attributed graphs). ...
ACKNOWLEDGMENT The authors would like to thank anonymous reviewers for their constructive and helpful comments. This work was supported partially by the U.S. ...
doi:10.1109/tkde.2020.2976063
fatcat:425zleslvrfjxklxecvyscbw7u
Blind equalization using the constant modulus criterion: a review
1998
Proceedings of the IEEE
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This paper provides a tutorial introduction to the constant modulus (CM) criterion for blind fractionally spaced equalizer (FSE) design via a (stochastic) gradient descent algorithm such as the constant ...
ACKNOWLEDGMENT The authors would like to thank the following people for their comments on earlier versions of this paper: C. ...
Thus, we consider the alternate formulation of the "decimated fractionally spaced convolution matrix" in (6) as essentially equivalent to in (3) . ...
doi:10.1109/5.720246
fatcat:lwf27wfvbjgmph7c32dewno44i
2021 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 14
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
., +, JSTARS 2021 1333-1347 Affinity Matrix Learning Via Nonnegative Matrix Factorization for Hyperspectral Imagery Clustering. ...
., +, JSTARS 2021 7410-7421 Affinity Matrix Learning Via Nonnegative Matrix Factorization for Hyperspectral Imagery Clustering. ...
doi:10.1109/jstars.2022.3143012
fatcat:dnetkulbyvdyne7zxlblmek2qy
Deep learning applications in single-cell omics data analysis
[article]
2021
bioRxiv
pre-print
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Single-cell (SC) omics are often high-dimensional, sparse, and complex, making DL techniques ideal for analyzing and processing such data. ...
However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) but lacking the needed biological interpretability in many ...
Another data integration model is inteGrative anaLysis of mUlti-omics at single-cEll Resolution (GLUER) (Peng, Chen et al. 2021) , which employs three computational approaches: a nonnegative matrix factorization ...
doi:10.1101/2021.11.26.470166
fatcat:3bmpecoza5dedbmwm62jwhfm4e
Determination of the attenuation map in emission tomography
2003
Journal of Nuclear Medicine
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Combination of data acquired from different imagers suffers from the usual problems of working with multimodality images--namely, accurate co-registration from the different modalities and assignment of ...
or simultaneously on multimodality imaging systems. ...
ACKNOWLEDGMENTS The authors gratefully thank Dale Bailey, PhD, of the Department of Nuclear Medicine, Royal North Shore Hospital, Sydney, Australia, for the valuable comments and suggestions he made to ...
pmid:12571222
fatcat:jhtuj3jelzbizlb7iqt22qs2fq
High-Order Laplacian Regularized Low-Rank Representation for Multimodal Dementia Diagnosis
2021
Frontiers in Neuroscience
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The existing methods usually fail to well handle these heterogeneous and noisy multimodal data for automated brain dementia diagnosis. ...
To this end, we propose a high-order Laplacian regularized low-rank representation method for dementia diagnosis using block-wise missing multimodal data. ...
methods (iMSF) (Yuan et al., 2012) with logistic loss (denoted as iMSF-1) and square loss (denoted as iMSF-2) and convolutional nonnegative matrix factorization (CH-CNMF) (Vaz et al., 2016) , deep ...
doi:10.3389/fnins.2021.634124
pmid:33776639
pmcid:PMC7994898
fatcat:han2bjyitnce7m6xylssd5mv3y
A Review about RNA–protein-binding Sites Prediction Based on Deep Learning
2020
IEEE Access
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In iONMF [32] , k-mer sequence, secondary structure, CLIP co-binding, GO information, and region type were integrated to predict RBP binding sites, using orthogonalityregularized nonnegative matrix factorization ...
A group of motifs is used for separate analysis. ...
doi:10.1109/access.2020.3014996
fatcat:s3uz4g2qh5dxppdm3abkayr6sq
2021 Index IEEE Transactions on Neural Networks and Learning Systems Vol. 32
2021
IEEE Transactions on Neural Networks and Learning Systems
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The Author Index contains the primary entry for each item, listed under the first author's name. ...
Wang, Blind source separation Nonnegative Blind Source Separation for Ill-Conditioned Mixtures via John Ellipsoid. ...
., +, TNNLS Feb. 2021 814-825 Nonnegative Blind Source Separation for Ill-Conditioned Mixtures via John Ellipsoid. ...
doi:10.1109/tnnls.2021.3134132
fatcat:2e7comcq2fhrziselptjubwjme
A Birds Eye View on Knowledge Graph Embeddings, Software Libraries, Applications and Challenges
[article]
2022
arXiv
pre-print
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We discuss existing KGC approaches, including the state-of-the-art Knowledge Graph Embeddings (KGE), not only on static graphs but also for the latest trends such as multimodal, temporal, and uncertain ...
Subsequently, we explored popular software packages for model training and examine open research challenges that can guide future research. ...
The idea was to utilize multiplicative update rules to extend the nonnegative factorization such that the triplets that contain textual knowledge are encoded in a matrix by tokenizing and stemming operations ...
arXiv:2205.09088v1
fatcat:c4gfzg4ldras3axpf5wvbldstm
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