L1-Norm Distance Linear Discriminant Analysis Based on an Effective Iterative Algorithm
Recent works have proposed two L1-norm distance measure-based linear discriminant analysis (LDA) methods, L1-LD and LDA-L1, which aim to promote the robustness of the conventional LDA against outliers. In LDA-L1, a gradient ascending iterative algorithm is applied, which, however, suffers from the c...
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| creator | Ye, Qiaolin Yang, Jian Liu, Fan Zhao, Chunxia Ye, Ning Yin, Tongming |
| description | Recent works have proposed two L1-norm distance measure-based linear discriminant analysis (LDA) methods, L1-LD and LDA-L1, which aim to promote the robustness of the conventional LDA against outliers. In LDA-L1, a gradient ascending iterative algorithm is applied, which, however, suffers from the choice of stepwise. In L1-LDA, an alternating optimization strategy is proposed to overcome this problem. In this paper, however, we show that due to the use of this strategy, L1-LDA is accompanied with some serious problems that hinder the derivation of the optimal discrimination for data. Then, we propose an effective iterative framework to solve a general L1-norm minimization-maximization ( minmax ) problem. Based on the framework, we further develop a effective L1-norm distance-based LDA (called L1-ELDA) method. Theoretical insights into the convergence and effectiveness of our algorithm are provided and further verified by extensive experimental results on image databases. |
| doi_str_mv | 10.1109/TCSVT.2016.2596158 |
| format | Article |
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In LDA-L1, a gradient ascending iterative algorithm is applied, which, however, suffers from the choice of stepwise. In L1-LDA, an alternating optimization strategy is proposed to overcome this problem. In this paper, however, we show that due to the use of this strategy, L1-LDA is accompanied with some serious problems that hinder the derivation of the optimal discrimination for data. Then, we propose an effective iterative framework to solve a general L1-norm minimization-maximization ( minmax ) problem. Based on the framework, we further develop a effective L1-norm distance-based LDA (called L1-ELDA) method. 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Theoretical insights into the convergence and effectiveness of our algorithm are provided and further verified by extensive experimental results on image databases.</description><subject>Algorithms</subject><subject>Convergence</subject><subject>Discriminant analysis</subject><subject>Distance measurement</subject><subject>Forestry</subject><subject>Iterative algorithms</subject><subject>Iterative framework</subject><subject>Iterative methods</subject><subject>L1-ELDA</subject><subject>L1-norm distance measure-based linear discriminant analysis (LDA)</subject><subject>Linear discriminant analysis</subject><subject>Optimization</subject><subject>Outliers (statistics)</subject><subject>Principal component analysis</subject><subject>Robustness</subject><issn>1051-8215</issn><issn>1558-2205</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kM1OwzAQhC0EEqXwAnCxxDnF3nhT51hKgUoRHChwtFxnDanapNgpUt-e9EecdlaaWe18jF1LMZBS5Hez8dvHbABCZgPAPJOoT1hPIuoEQOBppwXKRIPEc3YR40IIqbQa9thnIZOXJqz4QxVbWzviRVWTDbvdhWpV1bZu-ai2y22sIr-3kUre1NzWfOI9ubb6JT5tKdi9Gi2_mlC136tLdubtMtLVcfbZ--NkNn5Oiten6XhUJA5ybBPptNdz4YAoLVXmMg_ZPEUvSM1dSdkcco8kiVQpELT2me0cKaagBJRWpX12e7i7Ds3PhmJrFs0mdO9GA3KoEEGC6FxwcLnQxBjIm3XXzYatkcLsAJo9QLMDaI4Au9DNIVQR0X9giGmOmKd_EPVslA</recordid><startdate>201801</startdate><enddate>201801</enddate><creator>Ye, Qiaolin</creator><creator>Yang, Jian</creator><creator>Liu, Fan</creator><creator>Zhao, Chunxia</creator><creator>Ye, Ning</creator><creator>Yin, Tongming</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| subjects | Algorithms Convergence Discriminant analysis Distance measurement Forestry Iterative algorithms Iterative framework Iterative methods L1-ELDA L1-norm distance measure-based linear discriminant analysis (LDA) Linear discriminant analysis Optimization Outliers (statistics) Principal component analysis Robustness |
| title | L1-Norm Distance Linear Discriminant Analysis Based on an Effective Iterative Algorithm |
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