To tackle these problems, in this paper, the class information is incorporated into the framework of CCA for recognition, and a novel method for multimodal ...
Multimodal recognition emerges when the non- robustness of unimodal recognition is noticed in real applications. Canonical correlation analysis (CCA) is.
To tackle these problems, in this paper, the class information is incorporated into the framework of CCA for recognition, and a novel method for multimodal ...
Feb 28, 2021 · Abstract—In this paper, we propose the Discriminative Mul- tiple Canonical Correlation Analysis (DMCCA) for multimodal.
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PDF | Two new methods for dealing with missing values in generalized canonical correlation analysis are introduced. The first approach, which does not.
Missing: Discriminative | Show results with:Discriminative
Jun 11, 2022 · To solve this problem, here we propose a quantum DCCA algorithm. Specifically, we devise an efficient method to compute the mean of all samples, ...
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Based on the canonical correlation analysis (CCA), a supervised learning algorithm, sparsity preserving CCA with missing samples (SPCCAM), is proposed.
Canonical correlation analysis often used to analyze the correlation between two random vectors, cannot be performed on a dataset with missing data. Canonical ...
Missing: Discriminative | Show results with:Discriminative
A method is presented for generalized canonical correlation analysis of two or more matrices with missing rows. The method is a combination of Carroll̵.
In this paper, we propose the Discriminative Multiple Canonical Correlation Analysis (DMCCA) for multimodal information analysis and fusion.