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Multiview Uncorrelated Locality Preserving Projection.

Multiview Uncorrelated Locality Preserving Projection. Canonical Correlation Analysis (CCA) is a popular multiview dimension reduction method, which aims to maximize the correlation between two views to find the common subspace shared by these two views. However, it can only deal with two-view data, while the number of views frequently exceeds two in many real applications. To handle data with more than two views, in the previous studies, either the pairwise correlation or the high-order correlation was employed. These two types of correlation define the relation of multiview data from different viewpoints, and both have special effects for view consistency. To obtain flexible view consistency, in this article, we propose multiview uncorrelated locality preserving projection (MULPP), which considers two types of correlation simultaneously. The MULPP also considers the complementary property of different views by preserving the local structures of all the views. To obtain multiple projections and minimize the redundancy of low-dimensional features, for each view, the MULPP makes the features extracted by different projections uncorrelated. The MULPP is solved by an iteration algorithm, and the convergence of the algorithm is proven. The experiments on Multiple Feature, Coil-100, 3Sources, and NUS-WIDE data sets demonstrate the effectiveness of MULPP. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png IEEE transactions on neural networks and learning systems Pubmed

Multiview Uncorrelated Locality Preserving Projection.

IEEE transactions on neural networks and learning systems , Volume 31 (9): 14 – Sep 2, 2020

Multiview Uncorrelated Locality Preserving Projection.


Abstract

Canonical Correlation Analysis (CCA) is a popular multiview dimension reduction method, which aims to maximize the correlation between two views to find the common subspace shared by these two views. However, it can only deal with two-view data, while the number of views frequently exceeds two in many real applications. To handle data with more than two views, in the previous studies, either the pairwise correlation or the high-order correlation was employed. These two types of correlation define the relation of multiview data from different viewpoints, and both have special effects for view consistency. To obtain flexible view consistency, in this article, we propose multiview uncorrelated locality preserving projection (MULPP), which considers two types of correlation simultaneously. The MULPP also considers the complementary property of different views by preserving the local structures of all the views. To obtain multiple projections and minimize the redundancy of low-dimensional features, for each view, the MULPP makes the features extracted by different projections uncorrelated. The MULPP is solved by an iteration algorithm, and the convergence of the algorithm is proven. The experiments on Multiple Feature, Coil-100, 3Sources, and NUS-WIDE data sets demonstrate the effectiveness of MULPP.

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ISSN
2162-237X
eISSN
2162-2388
DOI
10.1109/TNNLS.2019.2944664
pmid
31670682

Abstract

Canonical Correlation Analysis (CCA) is a popular multiview dimension reduction method, which aims to maximize the correlation between two views to find the common subspace shared by these two views. However, it can only deal with two-view data, while the number of views frequently exceeds two in many real applications. To handle data with more than two views, in the previous studies, either the pairwise correlation or the high-order correlation was employed. These two types of correlation define the relation of multiview data from different viewpoints, and both have special effects for view consistency. To obtain flexible view consistency, in this article, we propose multiview uncorrelated locality preserving projection (MULPP), which considers two types of correlation simultaneously. The MULPP also considers the complementary property of different views by preserving the local structures of all the views. To obtain multiple projections and minimize the redundancy of low-dimensional features, for each view, the MULPP makes the features extracted by different projections uncorrelated. The MULPP is solved by an iteration algorithm, and the convergence of the algorithm is proven. The experiments on Multiple Feature, Coil-100, 3Sources, and NUS-WIDE data sets demonstrate the effectiveness of MULPP.

Journal

IEEE transactions on neural networks and learning systemsPubmed

Published: Sep 2, 2020

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