Convolutional 2D LDA for nonlinear dimensionality reduction

科研成果: 书/报告/会议事项章节会议稿件同行评审

22 引用 (Scopus)

摘要

Representing high-volume and high-order data is an essential problem, especially in machine learning field. Although existing two-dimensional (2D) discriminant analysis achieves promising performance, the single and linear projection features make it difficult to analyze more complex data. In this paper, we propose a novel convolutional two-dimensional linear discriminant analysis (2D LDA) method for data representation. In order to deal with nonlinear data, a specially designed Con-volutional Neural Networks (CNN) is presented, which can be proved having the equivalent objective function with common 2D LDA. In this way, the discriminant ability can benefit from not only the nonlinearity of Convolutional Neural Networks, but also the powerful learning process. Experiment results on several datasets show that the proposed method performs better than other state-of-the-art methods in terms of classification accuracy.

源语言英语
主期刊名26th International Joint Conference on Artificial Intelligence, IJCAI 2017
编辑Carles Sierra
出版商International Joint Conferences on Artificial Intelligence
2929-2935
页数7
ISBN(电子版)9780999241103
DOI
出版状态已出版 - 2017
活动26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, 澳大利亚
期限: 19 8月 201725 8月 2017

出版系列

姓名IJCAI International Joint Conference on Artificial Intelligence
0
ISSN(印刷版)1045-0823

会议

会议26th International Joint Conference on Artificial Intelligence, IJCAI 2017
国家/地区澳大利亚
Melbourne
时期19/08/1725/08/17

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