期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 46, 期 2, 页码 450-461出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2015.2403356
关键词
Feature selection; image classification; joint sparse learning; machine learning; multiview learning
类别
资金
- China 863 Program [2012AA011005]
- China 973 Program [2013CB329404]
- Natural Science Foundation of China [61170131, 61450001, 61263035]
- Guangxi Natural Science Foundation for Teams of Innovation and Research [2012GXNSFGA060004]
- Guangxi 100 Plan
- Guangxi Bagui Teams for Innovation and Research
- National Natural Science Foundation of China [61125106]
- Key Research Program of Chinese Academy of Sciences [KGZD-EW-T03]
In image analysis, the images are often represented by multiple visual features (also known as multiview features), that aim to better interpret them for achieving remarkable performance of the learning. Since the processes of feature extraction on each view are separated, the multiple visual features of images may include overlap, noise, and redundancy. Thus, learning with all the derived views of the data could decrease the effectiveness. To address this, this paper simultaneously conducts a hierarchical feature selection and a multiview multilabel (MVML) learning for multiview image classification, via embedding a proposed a new block-row regularizer into the MVML framework. The block-row regularizer concatenating a Frobenius norm (F-norm) regularizer and an l(2,1)-norm regularizer is designed to conduct a hierarchical feature selection, in which the F-norm regularizer is used to conduct a high-level feature selection for selecting the informative views (i.e., discarding the uninformative views) and the l(2,1)-norm regularizer is then used to conduct a low-level feature selection on the informative views. The rationale of the use of a block-row regularizer is to avoid the issue of the over-fitting (via the block-row regularizer), to remove redundant views and to preserve the natural group structures of data (via the F-norm regularizer), and to remove noisy features (the l(2,1)-norm regularizer), respectively. We further devise a computationally efficient algorithm to optimize the derived objective function and also theoretically prove the convergence of the proposed optimization method. Finally, the results on real image datasets show that the proposed method outperforms two baseline algorithms and three state-of-the-art algorithms in terms of classification performance.
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