Proceedings ArticleDOI
An overview of kernel based nonnegative matrix factorization
Viet-Hang Duong,Wen Chi Hsieh,Jia-Ching Wang +2 more
- pp 227-231
TLDR
This paper presents an overview of kernel methods on NMF along with its representation and recent variants, and discusses the development as well as algorithms for kernel based NMF.Abstract:
Nonnegative matrix factorization (NMF) is a recent method used to decompose a given data matrix into two nonnegative sparse factors. There are many techniques applied to enhance abilities of NMF, particularly kernel technique which discovering higher-order correlation between data points and obtaining more powerful latent features. This paper presents an overview of kernel methods on NMF along with its representation and recent variants. The development as well as algorithms for kernel based NMF are discussed and presented systematically.read more
Citations
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Journal Article
Non-negative matrix factorization on kernels
TL;DR: The original non-negative matrix factorization (NMF) is extended to kernel NMF (KNMF), which can deal with data where only relationships between objects are known and process data with negative values by using some specific kernel functions (e.g. Gaussian).
Proceedings ArticleDOI
Nonlinear non-negative matrix factorization using deep learning
TL;DR: A nonlinear NMF optimization model is constructed and the optimization algorithm is developed, and the experimental results on some benchmark dataset show the nonlinear dimension reduction helps the NMF to improve the clustering performance.
Journal ArticleDOI
Kernel Joint Non-Negative Matrix Factorization for Genomic Data
TL;DR: In this paper, a Kernel Non-negative Matrix Factorization (kernel jNMF) is proposed to incorporate the factorization of the original matrices into a high-dimensional space.
References
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Journal ArticleDOI
Learning the parts of objects by non-negative matrix factorization
TL;DR: An algorithm for non-negative matrix factorization is demonstrated that is able to learn parts of faces and semantic features of text and is in contrast to other methods that learn holistic, not parts-based, representations.
Learning parts of objects by non-negative matrix factorization
TL;DR: In this article, non-negative matrix factorization is used to learn parts of faces and semantic features of text, which is in contrast to principal components analysis and vector quantization that learn holistic, not parts-based, representations.
Journal ArticleDOI
Positive matrix factorization: A non-negative factor model with optimal utilization of error estimates of data values†
Pentti Paatero,Unto Tapper +1 more
TL;DR: In this paper, a new variant of Factor Analysis (PMF) is described, where the problem is solved in the weighted least squares sense: G and F are determined so that the Frobenius norm of E divided (element-by-element) by σ is minimized.
Journal ArticleDOI
Least squares formulation of robust non-negative factor analysis
TL;DR: Positive matrix factorization (PMF) is a recently published factor analytic technique where the left and right factor matrices (corresponding to scores and loadings) are constrained to non-negative values as mentioned in this paper.
Journal ArticleDOI
Domain Transfer Multiple Kernel Learning
Lixin Duan,Ivor W. Tsang,Dong Xu +2 more
TL;DR: Comprehensive experiments on three domain adaptation data sets demonstrate that DTMKL-based methods outperform existing cross-domain learning and multiple kernel learning methods.