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Laplacian multiset canonical correlations for multiview feature extraction and image recognition

机译：拉普拉斯多集规范相关性用于多视图特征提取和图像识别

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摘要

Multiset canonical correlation analysis (MCCA) aims at revealing the linear correlations among multiple sets of high-dimensional data. Therefore, it is only a linear multiview dimensionality reduction technique and such a linear model is insufficient to discover the nonlinear correlation information hidden in multiview data. In this paper, we incorporate the local structure information into MCCA and propose a novel algorithm for multiview dimensionality reduction, called Laplacian multiset canonical correlations (LapMCCs), which simultaneously considers local within-view and local between-view correlations by using nearest neighbor graphs. This makes LapMCC capable of discovering the nonlinear correlation information among multiview data by combining many locally linear problems together. Moreover, we also develop an orthogonal version of LapMCC to preserve the metric structure. The proposed LapMCC method is applied to face and object image recognition. The experimental results on AR, Yale-B, AT&T, and ETH-80 databases demonstrate the superior performance of LapMCC compared to existing multiview dimensionality reduction methods.
机译：多集规范相关分析（MCCA）旨在揭示多组高维数据之间的线性相关性。因此，仅是线性多视图降维技术，并且这种线性模型不足以发现隐藏在多视图数据中的非线性相关信息。在本文中，我们将局部结构信息纳入MCCA，并提出了一种用于多视图降维的新颖算法，称为拉普拉斯多集规范相关性（LapMCCs），该算法通过使用最近邻图同时考虑局部视图内和局部视图间相关性。这使得LapMCC能够通过将许多局部线性问题组合在一起来发现多视图数据之间的非线性相关信息。此外，我们还开发了LapMCC的正交版本以保留度量结构。提出的LapMCC方法应用于人脸和目标图像的识别。在AR，Yale-B，AT＆T和ETH-80数据库上的实验结果证明，与现有的多视图降维方法相比，LapMCC具有优越的性能。

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