Intra-View and Inter-View Supervised Correlation Analysis for Multi-View Feature Learning
Intra-View and Inter-View Supervised Correlation Analysis for Multi-View Feature Learning
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DOI：
10.1609/aaai.v28i1.8986
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发表时间：
2014-06
期刊：
影响因子：
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通讯作者：
Xiaoyuan Jing;R. Hu;Yang-Ping Zhu;Shanshan Wu;Chao Liang;Jing-yu Yang
中科院分区：
文献类型：
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作者：
Xiaoyuan Jing;R. Hu;Yang-Ping Zhu;Shanshan Wu;Chao Liang;Jing-yu Yang
多视图特征学习是一个有吸引力的研究课题，并取得了巨大的实际成功。典型相关分析（CCA）已成为多视图学习中的一项重要技术，因为它可以充分利用视图间的相关性。在本文中，我们主要研究基于CCA的多视图监督特征学习技术，其中训练样本的标签是已知的。已经提出了几种基于监督 CCA 的多视图方法，其重点是研究不同视图之间的监督相关性。然而，他们没有考虑样本之间的视图内相关性。研究人员还将判别分析技术引入到多视图特征学习中，例如多视图判别分析（MvDA）。但他们忽略了每个视图内以及所有视图之间的规范相关性。在本文中，我们提出了一种基于视图内和视图间监督相关分析（I2SCA）的新颖的多视图特征学习方法，该方法可以探索每个视图内以及所有视图之间样本的有用相关信息。 I2SCA 的目标函数旨在同时从视图间和视图内提取有区别的相关特征。无需迭代计算即可得到解析解。我们提供了 I2SCA 的内核化扩展来解决原始特征空间中的线性不可分问题。采用四个广泛使用的数据集作为测试数据。实验结果表明，我们提出的方法优于几种代表性的多视图监督特征学习方法。
Multi-view feature learning is an attractive research topic with great practical success. Canonical correlation analysis (CCA) has become an important technique in multi-view learning, since it can fully utilize the inter-view correlation. In this paper, we mainly study the CCA based multi-view supervised feature learning technique where the labels of training samples are known. Several supervised CCA based multi-view methods have been presented, which focus on investigating the supervised correlation across different views. However, they take no account of the intra-view correlation between samples. Researchers have also introduced the discriminant analysis technique into multi-view feature learning, such as multi-view discriminant analysis (MvDA). But they ignore the canonical correlation within each view and between all views. In this paper, we propose a novel multi-view feature learning approach based on intra-view and inter-view supervised correlation analysis (I2SCA), which can explore the useful correlation information of samples within each view and between all views. The objective function of I2SCA is designed to simultaneously extract the discriminatingly correlated features from both inter-view and intra-view. It can obtain an analytical solution without iterative calculation. And we provide a kernelized extension of I2SCA to tackle the linearly inseparable problem in the original feature space. Four widely-used datasets are employed as test data. Experimental results demonstrate that our proposed approaches outperform several representative multi-view supervised feature learning methods.