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A multi-label feature extraction algorithm via maximizing feature variance and feature-label dependence simultaneously

机译：同时最大化特征方差和特征标签依赖性的多标签特征提取算法

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摘要

Dimensionality reduction is an important pre-processing procedure for multi-label classification to mitigate the possible effect of dimensionality curse, which is divided into feature extraction and selection. Principal component analysis (PCA) and multi-label dimensionality reduction via dependence maximization (MDDM) represent two mainstream feature extraction techniques for unsupervised and supervised paradigms. They produce many small and a few large positive eigenvalues respectively, which could deteriorate the classification performance due to an improper number of projection directions. It has been proved that PCA proposed primarily via maximizing feature variance is associated with a least-squares formulation. In this paper, we prove that MDDM with orthonormal projection directions also falls into the least-squares framework, which originally maximizes Hilbert-Schmidt independence criterion (HSIC). Then we propose a novel multi-label feature extraction method to integrate two least-squares formulae through a linear combination, which maximizes both feature variance and feature-label dependence simultaneously and thus results in a proper number of positive eigenvalues. Experimental results on eight data sets show that our proposed method can achieve a better performance, compared with other seven state-of-the-art multi-label feature extraction algorithms. (C) 2016 Elsevier B.V. All rights reserved.
机译：降维是多标签分类的重要预处理过程，可减轻降维诅咒的可能影响，该过程分为特征提取和选择。主成分分析（PCA）和通过依赖最大化（MDDM）减少多标签维数是无监督和受监督范式的两种主流特征提取技术。它们分别产生许多小的和一些大的正特征值，这可能由于投影方向数量不正确而使分类性能恶化。已经证明，主要通过最大化特征方差提出的PCA与最小二乘公式有关。在本文中，我们证明具有正交投影方向的MDDM也属于最小二乘框架，该框架最初使Hilbert-Schmidt独立准则（HSIC）最大化。然后，我们提出了一种新颖的多标签特征提取方法，该方法通过线性组合来整合两个最小二乘公式，从而同时最大化特征方差和特征标签相关性，从而获得适当数量的正特征值。在八个数据集上的实验结果表明，与其他七个最新的多标签特征提取算法相比，我们提出的方法可以实现更好的性能。 （C）2016 Elsevier B.V.保留所有权利。

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