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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Mixture Subclass Discriminant Analysis Link to Restricted Gaussian Model and Other Generalizations
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Mixture Subclass Discriminant Analysis Link to Restricted Gaussian Model and Other Generalizations

机译：混合子类判别分析链接到受限高斯模型和其他概括

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摘要

In this paper, a theoretical link between mixture subclass discriminant analysis (MSDA) and a restricted Gaussian model is first presented. Then, two further discriminant analysis (DA) methods, i.e., fractional step MSDA (FSMSDA) and kernel MSDA (KMSDA) are proposed. Linking MSDA to an appropriate Gaussian model allows the derivation of a new DA method under the expectation maximization (EM) framework (EM-MSDA), which simultaneously derives the discriminant subspace and the maximum likelihood estimates. The two other proposed methods generalize MSDA in order to solve problems inherited from conventional DA. FSMSDA solves the subclass separation problem, that is, the situation in which the dimensionality of the discriminant subspace is strictly smaller than the rank of the inter-between-subclass scatter matrix. This is done by an appropriate weighting scheme and the utilization of an iterative algorithm for preserving useful discriminant directions. On the other hand, KMSDA uses the kernel trick to separate data with nonlinearly separable subclass structure. Extensive experimentation shows that the proposed methods outperform conventional MSDA and other linear discriminant analysis variants.
机译：本文首先提出了混合子类判别分析（MSDA）与受限高斯模型之间的理论联系。然后，提出了另外两种判别分析（DA）方法，即分数步MSDA（FSMSDA）和核MSDA（KMSDA）。将MSDA链接到适当的高斯模型可以在期望最大化（EM）框架（EM-MSDA）下推导新的DA方法，该方法同时可以导出判别子空间和最大似然估计。提出的另外两种方法一般化MSDA，以解决从常规DA继承的问题。 FSMSDA解决了子类分离问题，即判别子空间的维数严格小于子类间散布矩阵的秩的情况。这是通过适当的加权方案和使用迭代算法来保留有用的判别方向来完成的。另一方面，KMSDA使用内核技巧来分离具有非线性可分离子类结构的数据。大量的实验表明，所提出的方法优于常规的MSDA和其他线性判别分析方法。

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