Deep Tensor CCA for Multi-view Learning

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Detail(s)

Original languageEnglish
Pages (from-to)1664-1677
Journal / PublicationIEEE Transactions on Big Data
Volume8
Issue number6
Online published11 May 2021
Publication statusPublished - Nov 2022

Abstract

We present Deep Tensor Canonical Correlation Analysis (DTCCA), a method to learn complex nonlinear transformations of multiple views (more than two) of data such that the resulting representations are linearly correlated in high order. The high-order correlation of given multiple views is modeled by covariance tensor, which is different from most CCA formulations relying solely on the pairwise correlations. Parameters of transformations of each view are jointly learned by maximizing the high-order canonical correlation. To solve the resulting problem, we reformulate it as the best sum of rank-1 approximation, which can be efficiently solved by existing tensor decomposition method. DTCCA is a nonlinear extension of tensor CCA (TCCA) via deep networks. Comparing with kernel TCCA, DTCCA not only can deal with arbitrary dimensions of the input data, but also does not need to maintain the training data for computing representations of any given data point. Hence, DTCCA as a unified model can efficiently overcome the scalable issue of TCCA for either high-dimensional multi-view data or a large amount of views, and it also naturally extends TCCA for learning nonlinear representation. Extensive experiments on four multi-view data sets demonstrate the effectiveness of the proposed method.

Research Area(s)

  • canonical correlation analysis, Correlation, deep networks, Ear, Kernel, Multi-view learning, Pairwise error probability, tensor decomposition, Tensors, Training data, Web pages

Citation Format(s)

Deep Tensor CCA for Multi-view Learning. / Wong, Hok Shing; Wang, Li; Chan, Raymond et al.

In: IEEE Transactions on Big Data, Vol. 8, No. 6, 11.2022, p. 1664-1677.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review