Multiple Marginal Fisher Analysis

Multiple Marginal Fisher Analysis
Title:
Multiple Marginal Fisher Analysis
Other Titles:
IEEE Transactions on Industrial Electronics
Publication Date:
28 September 2018
Citation:
Z. Huang, H. Zhu, J. T. Zhou and X. Peng, "Multiple Marginal Fisher Analysis," in IEEE Transactions on Industrial Electronics, vol. 66, no. 12, pp. 9798-9807, Dec. 2019, doi: 10.1109/TIE.2018.2870413.
Abstract:
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.
License type:
PublisherCopyrights
Funding Info:
The work of Z. Huang and X. Peng was supported in part by the Fundamental Research Funds for the Central Universities under Grant YJ201748 and in part by the NFSC under Grant 61806135, Grant 61432012, and Grant U1435213. The work of J. T. Zhou was supported by RIE2020 Plan under Grant A1687b0033.
Description:
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ISSN:
0278-0046
1557-9948
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