Hybrid Dimensionality Reduction Method Based on Support Vector Machine and Independent Component Analysis

Sangwoo Moon

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Hybrid Dimensionality Reduction Method Based on Support Vector Machine and Independent Component Analysis

机译：基于支持向量机和独立分量分析的混合降维方法

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This paper presents a new hybrid dimensionality reduction method to seek projection through optimization of both structural risk (supervised criterion) and data independence (unsupervised criterion). Classification accuracy is used as a metric to evaluate the performance of the method. By minimizing the structural risk, projection originated from the decision boundaries directly improves the classification performance from a supervised perspective. From an unsupervised perspective, projection can also be obtained based on maximum independence among features (or attributes) in data to indirectly achieve better classification accuracy over more intrinsic representation of the data. Orthogonality interrelates the two sets of projections such that minimum redundancy exists between the projections, leading to more effective dimensionality reduction. Experimental results show that the proposed hybrid dimensionality reduction method that satisfies both criteria simultaneously provides higher classification performance, especially for noisy data sets, in relatively lower dimensional space than various existing methods.

机译：本文提出了一种新的混合降维方法，该方法通过优化结构风险（有监督的标准）和数据独立性（无监督的标准）来寻找投影。分类精度用作评估方法性能的指标。通过最小化结构风险，从监督边界出发的投影直接从监督的角度提高了分类性能。从无监督的角度来看，还可以基于数据中特征（或属性）之间的最大独立性来获得投影，从而间接地在数据的更多固有表示上实现更好的分类精度。正交使两组投影相互关联，以使在投影之间存在最小的冗余，从而导致更有效的降维。实验结果表明，所提出的同时满足这两个标准的降维方法比现有的各种方法在较低的维数空间内提供了更高的分类性能，尤其是对于嘈杂的数据集。

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《Neural Networks and Learning Systems, IEEE Transactions on》 |2012年第5期|p.749-761|共13页
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Hybrid Dimensionality Reduction Method Based on Support Vector Machine and Independent Component Analysis

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