Infinite Max-margin Linear Discriminant Projection Model

WEN Wei; CAO Xuefei; CHEN Bo; HAN Xun; ZHANG Xuefeng; WANG Penghui; LIU Hongwei

doi:10.11999/JEIT170256

Volume 39 Issue 12

Dec. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(12): 2795-2802

WEN Wei, CAO Xuefei, CHEN Bo, HAN Xun, ZHANG Xuefeng, WANG Penghui, LIU Hongwei. Infinite Max-margin Linear Discriminant Projection Model[J]. Journal of Electronics & Information Technology, 2017, 39(12): 2795-2802. doi: 10.11999/JEIT170256

Citation:

WEN Wei, CAO Xuefei, CHEN Bo, HAN Xun, ZHANG Xuefeng, WANG Penghui, LIU Hongwei. Infinite Max-margin Linear Discriminant Projection Model[J]. Journal of Electronics & Information Technology, 2017, 39(12): 2795-2802. doi: 10.11999/JEIT170256

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Infinite Max-margin Linear Discriminant Projection Model

doi: 10.11999/JEIT170256

Funds:

The National Science Fund for Distinguished Young Scholars (61525105), The National Natural Science Foundation of China (61201292, 61322103, 61372132), The Program for New Century Excellent Talents in University (FANEDD-201156), The Natural Science Basic Research Plan in Shaanxi Province (2016JQ6048), The Avaation Science Fund (20142081009), Shanghai Aerospca Science, Technology Innovation Fund (SAST2015009), The Key Laboratory Fund of RF Integrated Laboratory in Avionics System

Received Date: 2017-03-29
Rev Recd Date: 2017-09-21
Publish Date: 2017-12-19

Abstract

Abstract

An infinite Max-Margin Linear Discriminant Projection (iMMLDP) model is developed to deal with the classification problem on multimodal distributed high-dimensional data. Different from global projection, iMMLDP divides the data into a set of local regions via Dirichlet Process (DP) mixture model and meanwhile learns a linear Max-Margin Linear Discriminant Projection (MMLDP) classifier in each local region. By assembling these local classifiers, a flexible nonlinear classifier is constructed. Under this framework, iMMLDP combines dimensionality reduction, clustering and supervised classification in a principled way, therefore, an underlying structure of the data could be uncovered. As a result, the model can handle the classification of data with global nonlinear structure, especially the data with multi-modally distributed structure. With the help of Bayesian nonparametric prior, the model selection problem (e.g. the number of local regions) can be avoided. The proposed model is implemented on synthesized and real-world data, including multi-modally distributed datasets and measured radar high range resolution profile (HRRP) data, to validate its efficiency and effectiveness.
- Max-Margin Linear Discriminant Projection (MMLDP),
- Bayesian nonparametric,
- Dirichlet Process Mixture (DPM) model

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References(26)

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