期刊
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 66, 期 12, 页码 9798-9807出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2018.2870413
关键词
Automatic dimension reduction; graph embedding; manifold learning; supervised subspace learning
资金
- Fundamental Research Funds for the Central Universities [YJ201748]
- NFSC [61806135, 61432012, U1435213]
- RIE2020 Plan [A1687b0033]
Dimension reduction is a fundamental task of machine learning and computer vision, which is widely used in a variety of industrial applications. Over past decades, a lot of unsupervised and supervised algorithms have been proposed. However, few of them can automatically determine the feature dimension that could be adaptive to different data distributions. To obtain a good performance, it is popular to seek the optimal dimension by exhaustively enumerating some possible values. Clearly, such a scheme is ad hoc and computationally extensive. Therefore, a method which can automatically estimate the feature dimension in an efficient and principled manner is of significant practical and theoretical value. In this paper, we propose a novel supervised subspace learning method called multiple marginal Fisher analysis (MMFA), which can automatically estimate the feature dimension. By maxing the interclass separability among marginal points while minimizing within-class scatter, MMFA obtains low-dimensional representations with outstanding discriminative properties. Extensive experiments show that MMFA not only outperforms other algorithms on clean data, but also show robustness on corrupted and disguised data.
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