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Tensor Canonical Correlation Analysis on Graphs

Reddy, S and Chepuri, SP (2021) Tensor Canonical Correlation Analysis on Graphs. In: 55th Asilomar Conference on Signals, Systems and Computers, ACSSC 2021, 31 October 2021 - 3 November 2021, Virtual, Pacific Grove, pp. 1546-1550.

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Official URL: https://doi.org/10.1109/IEEECONF53345.2021.9723398


In this work, we focus on canonical correlation analysis (CCA) for tensor dataset pairs. To handle tensor datasets, traditional CCA requires vectorization of the tensor data. By doing so, we lose the intrinsic structure in the data. Vectorization also leads to a significant increase in the dataset size and thereby increasing the computational complexity. The low-dimensional representations of the data often have a structure that a graph can conveniently capture. This paper proposes tensor graph CCA (TGCCA) that generalizes CCA to handle tensor data while incorporating a graph structure in the canonical variates through a graph regularizer. We present an alternating minimization solver to learn the canonical subspaces, where we learn the canonical subspaces corresponding to a mode of the tensor using a partial singular value decomposition as in the classical CCA, while keeping the other canonical subspaces fixed. Through experiments on real datasets for correspondence learning, we demonstrate the benefit of leveraging the graph structure of the canonical variates and directly working with tensor data. © 2021 IEEE.

Item Type: Conference Proceedings
Publication: Conference Record - Asilomar Conference on Signals, Systems and Computers
Publisher: IEEE Computer Society
Additional Information: The copyright for this article belongs to IEEE Computer Society
Keywords: Computer vision; Correlation methods; Graphic methods; Singular value decomposition; Vectors, Canonical correlations analysis; Data set size; Dimensionality reduction; Graph structured data; Graph structures; Intrinsic structures; Learn+; Low-dimensional representation; Subspace learning; Vectorization, Tensors
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Division of Electrical Sciences > Electrical Communication Engineering
Date Deposited: 19 May 2022 09:47
Last Modified: 19 May 2022 09:47
URI: https://eprints.iisc.ac.in/id/eprint/71911

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