NDLI: Robust non-negative matrix factorization via joint sparse and graph regularization for transfer learning

Content Provider	Springer Nature Link
Author	Yang, Shizhun Hou, Chenping Zhang, Changshui Wu, Yi
Copyright Year	2013
Abstract	In real-world applications, we often have to deal with some high-dimensional, sparse, noisy, and non-independent identically distributed data. In this paper, we aim to handle this kind of complex data in a transfer learning framework, and propose a robust non-negative matrix factorization via joint sparse and graph regularization model for transfer learning. First, we employ robust non-negative matrix factorization via sparse regularization model (RSNMF) to handle source domain data and then learn a meaningful matrix, which contains much common information between source domain and target domain data. Second, we treat this learned matrix as a bridge and transfer it to target domain. Target domain data are reconstructed by our robust non-negative matrix factorization via joint sparse and graph regularization model (RSGNMF). Third, we employ feature selection technique on new sparse represented target data. Fourth, we provide novel efficient iterative algorithms for RSNMF model and RSGNMF model and also give rigorous convergence and correctness analysis separately. Finally, experimental results on both text and image data sets demonstrate that our REGTL model outperforms existing start-of-art methods.
Starting Page	541
Ending Page	559
Page Count	19
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	23
Issue Number	2
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2013-03-15
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Transfer learning Non-negative matrix factorization Sparse regularization Graph regularization Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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