无限最大间隔线性判别投影模型

文伟; 曹雪菲; 陈渤; 韩勋; 张学峰; 王鹏辉; 刘宏伟

doi:10.11999/JEIT170256

无限最大间隔线性判别投影模型

doi: 10.11999/JEIT170256

基金项目:

国家杰出青年科学基金(61525105)，国家自然科学基金(61201292, 61322103, 61372132)，全国优秀博士学位论文作者专项资金(FANEDD-201156)，陕西省自然科学基础研究计划(2016JQ6048)，航空科学基金(20142081009)，上海航天科技创新基金(SAST2015009)，航空电子系统射频综合仿真航空科技重点实验室基金

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出版历程
- 收稿日期: 2017-03-29
- 修回日期: 2017-09-21
- 刊出日期: 2017-12-19

Infinite Max-margin Linear Discriminant Projection Model

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

摘要

摘要: 针对具有多模分布结构的高维数据的分类问题，该文提出一种无限最大间隔线性判别投影(iMMLDP)模型。与现有全局投影方法不同，模型通过联合Dirichlet过程及最大间隔线性判别投影(MMLDP)模型将数据划分为若干个局部区域，并在每一个局部学习一个最大边界线性判别投影分类器。组合各局部分类器，实现全局非线性的投影与分类。iMMLDP模型利用贝叶斯框架联合建模，将聚类、投影及分类器进行联合学习，可以有效发掘数据的隐含结构信息，因而，可以较好地对非线性可分数据，尤其是具有多模分布特性数据进行分类。得益于非参数贝叶斯先验技术，可以有效避免模型选择问题，即局部区域划分数量。基于仿真数据集、公共数据集及雷达实测数据集验证了所提方法的有效性。
- 最大间隔线性判别投影 /
- 非参数贝叶斯 /
- Dirichlet过程混合模型
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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参考文献(26)

DUDA R, HART P, and STORK D. Pattern Classification (2nd ed.)[M]. New York, Wiley Interscience, 2000: 106-113.

郁道银, 王悦行, 陈晓冬, 等. 基于随机投影和稀疏表示的追踪算法[J]. 电子与信息学报, 2016, 38(7): 1602-1608. doi: 10.11999/JEIT151064.

YU Daoyin, WANG Yuexing, CHEN Xiaodong, et al. Visual tracking based on random projection and sparse representation[J]. Journal of Electronics Information Technology, 2016, 38(7): 1602-1608. doi: 10.11999/JEIT 151064.

SWETS D and WENG J. Using discriminant eigenfeatures for image retrieval[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1996, 18(8): 831-836. doi: 10.1109/34.531802.

ETEMAD K and CHELLAPA R. Discriminant analysis for recognition of human face images[J]. Journal of the Optical Society of America A, 1997, 14(8): 1724-1733. doi: 10.1364 /JOSAA.14.001724.

FISHER R. The use of multiple measurements in taxonomic problems [J]. Annals of Eugenics, 1936, 7(2): 179188 doi: 10.1111/j.1469-1809.1936.tb02137.

CHEN B, ZHANG H, ZHANG X, et al. Max-margin discriminant projection via data augmentation[J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(7): 1964-1976. doi: 10.1109/TKDE.2015.2397444.

NIKOLAOS G, VASILEIOS M, and IOANNIS K. Mixture Subclass discriminant analysis[J]. IEEE Signal Processing Letter, 2011, 18(5): 319-322. doi: 10.1109/LSP.2011. 2127474.

NIKOLAOS G, VASILEIOS M, and IOANNIS K. Mixture subclass discriminant analysis link to restricted Gaussian model and other generalizations[J]. IEEE Transactions on Neural Networks and Learning Systems, 2013, 24(1): 8-21. doi: 10.1109/TNNLS.2012.2216545.

郭继昌, 张帆, 王楠. 基于Fisher约束和字典对的图像分类 [J] 电子与信息学报, 2017, 39(2): 270-277. doi: 10.11999/ JEIT160329.

GUO Jichang, ZHANG Fan, and WANG Nan. Image classification based on Fisher constraint and dictionary pair [J]. Journal of Electronics Information Technology, 2017, 39(2): 270-277. doi: 10.11999/JEIT160329.

GONEN M. Bayesian supervised dimensionality reduction [J]. IEEE Transactions on Cybernetics, 2013, 4(6): 2179-2189. doi: 10.1109/TCYB.2013.2245321.

SHAHBABA B and NEAL R. Nonlinear models using Dirichlet process mixtures[J]. The Journal of Machine Learning Research, 2009, 10(4): 1829-1850.

文伟, 曹雪菲, 张学峰, 等. 一种基于多极化散射机理的极化SAR图像舰船目标检测方法[J]. 电子与信息学报, 2017, 39(1): 103-109. doi: 10.11999/JEIT160204.

WEN Wei, CAO Xuefei, ZHANG Xuefeng, et al. PolSAR ship detection method based on multiple polarimetric scattering mechanisms[J]. Journal of Electronics Information Technology, 2017, 39(1): 103-109. doi: 10.11999 /JEIT160204.

POLSON N G and SCOTT S L. Data augmentation for support vector machines[J]. Bayesian Analysis, 2011, 6(1): 1-24. doi: 10.1214/11-BA601.

SETHURAMAN J. A constructive definition of Dirichlet priors[J]. Statistica Sinica, 1994, 4(2): 639-650.

HANNAH L A, BLEI D M, and POWELL W B. Dirichlet process mixtures of generalized linear models[J]. Journal of Machine Learning Research, 2011, 12: 1923-1953.

RIFKIN R and KLAUTAU A. In defense of one-vs-all classification[J]. Journal of Machine Learning Research, 2004, 5(1): 101-141.

DU L, LIU H W, BO Z, et al. Radar HRRP statistical recognition: Parametric model and model selection[J]. IEEE Transactions on Signal Processing, 2008, 56 (5): 1931-1943. doi: 10.1109/TSP.2007.912283.

张学峰, 陈渤, 王鹏辉, 等. 无限最大间隔Beta过程因子分析模型[J]. 西安电子科技大学学报(自然科学版), 2016, 43(3): 13-18. doi: 10.3969/j.issn.1001-2400.2016.03.003.

ZHANG Xuefeng, CHEN BO, WANG Penghui, et al. Infinite max-margin Beta process factor analysis model[J]. Journal of Xidian University (Natural Science), 2016, 43(3): 13-18. doi: 10.3969/j.issn.1001-2400.2016.03.003.

DU L, LIU H W, WANG P H, et al. Noise robust radar HRRP target recognition based on multitask factor analysis with small training data size[J]. IEEE Transactions on Signal Processing, 2012, 60(7): 3546-3559. doi: 10.1109/TSP. 2012.2191965.

张学峰, 陈渤, 王鹏辉, 等. 一种基于Dirichlet过程隐变量支撑向量机模型的目标识别方法[J]. 电子与信息学报, 2015, 37(1): 29-36. doi: 10.11999/JEIT140129.

ZHANG Xuefeng, CHEN Bo, WANG Penghui, et al. A

target recognition method based on Dirichlet process latent variable support vector machine model[J]. Journal of Electronics Information Technology, 2015, 37(1): 29-36. doi: 10.11999/JEIT140129.

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