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Feature selection and multi-kernel learning for adaptive graph regularized nonnegative matrix factorization
- Source :
- Expert Systems with Applications. 42(3):1278-1286
- Publication Year :
- 2015
- Publisher :
- Elsevier BV, 2015.
-
Abstract
- Graph has been used to regularize nonnegative matrix factorization (NMF).However, noisy features and nonlinear distributed data effect the graph construction.We proposed to integrate feature selection and multi-kernel learning to this problem.Novel algorithms are developed to learn feature/kernel weights and NMF parameters. Nonnegative matrix factorization (NMF), a popular part-based representation technique, does not capture the intrinsic local geometric structure of the data space. Graph regularized NMF (GNMF) was recently proposed to avoid this limitation by regularizing NMF with a nearest neighbor graph constructed from the input data set. However, GNMF has two main bottlenecks. First, using the original feature space directly to construct the graph is not necessarily optimal because of the noisy and irrelevant features and nonlinear distributions of data samples. Second, one possible way to handle the nonlinear distribution of data samples is by kernel embedding. However, it is often difficult to choose the most suitable kernel. To solve these bottlenecks, we propose two novel graph-regularized NMF methods, AGNMFFS and AGNMFMK, by introducing feature selection and multiple-kernel learning to the graph regularized NMF, respectively. Instead of using a fixed graph as in GNMF, the two proposed methods learn the nearest neighbor graph that is adaptive to the selected features and learned multiple kernels, respectively. For each method, we propose a unified objective function to conduct feature selection/multi-kernel learning, NMF and adaptive graph regularization simultaneously. We further develop two iterative algorithms to solve the two optimization problems. Experimental results on two challenging pattern classification tasks demonstrate that the proposed methods significantly outperform state-of-the-art data representation methods.
- Subjects :
- 0209 industrial biotechnology
Graph kernel
Optimization problem
Feature vector
Feature selection
02 engineering and technology
Non-negative matrix factorization
Nonnegative matrix factorization
020901 industrial engineering & automation
Nearest neighbor graph
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
Adjacency matrix
Engineering(all)
Mathematics
Multi-kernel learning
business.industry
General Engineering
Data representation
Pattern recognition
Computer Science Applications
Graph regularization
Graph (abstract data type)
020201 artificial intelligence & image processing
Artificial intelligence
business
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 42
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi.dedup.....f8b58c846b0ffbbb8d109fc3180a5363
- Full Text :
- https://doi.org/10.1016/j.eswa.2014.09.008