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Locality sensitive discriminant matrixized learning machine

机译：局部敏感判别矩阵学习机

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Differently from Vector-pattern-oriented Classifier Design (VecCD), Matrix-pattern-oriented Classifier Design (MatCD) is expected to manipulate matrix-oriented patterns directly rather than turning them into a vector, and further demonstrated its effectiveness. However, some prior information, such as the local sensitive discriminant information among matrix-oriented patterns, might be neglected by MatCD. To overcome such flaw, a new regularization term named Rim is adopted into MatCD by taking advantage of Locality Sensitive Discriminant Analysis (LSDA) in this paper. In detail, the objective function of LSDA is modified and transformed into the regularization term RED to explore the local sensitive discriminant information among matrix-oriented patterns. In the implementation, R-LSD is collaborated with one typical MatCD, whose name is Matrix-pattern-oriented Ho-Kashyap Classifier (MatMHKS), so as to create a new classifier based on local sensitive discriminant information named LSDMatMHKS for short. Finally, comprehensive experiments are designed to validate the effectiveness of LSDMatMHKS. The major contributions of this paper can be concluded as (1) improving the classification performance and the learning ability of MatCD, (2) introducing local sensitive discriminant information into MatCD and extending the application scenario of LSDA, and (3) validating and analyzing the feasibility and effectiveness of R-LSD). (C) 2016 Elsevier B.V. All rights reserved.
机译：与面向矢量模式的分类器设计（VecCD）不同，面向矩阵模式的分类器设计（MatCD）有望直接处理面向矩阵的模式，而不是将其转换为矢量，从而进一步证明其有效性。但是，MatCD可能会忽略某些先验信息，例如面向矩阵的模式之间的局部敏感判别信息。为了克服这种缺陷，本文利用局部敏感判别分析（LSDA）在MatCD中采用了一个新的正则化术语Rim。详细地，LSDA的目标函数被修改并转化为正则化项RED，以探索面向矩阵的模式之间的局部敏感判别信息。在实现中，R-LSD与一个典型的MatCD（名称为面向矩阵模式的Ho-Kashyap分类器（MatMHKS））协作，以便基于名为LSDMatMHKS的本地敏感判别信息创建一个新的分类器。最后，设计了综合实验以验证LSDMatMHKS的有效性。本文的主要贡献可以归结为：（1）提高MatCD的分类性能和学习能力，（2）将局部敏感的判别信息引入MatCD，扩展LSDA的应用场景，（3）验证和分析R-LSD的可行性和有效性）。 （C）2016 Elsevier B.V.保留所有权利。

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