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Generalized canonical correlation

About: Generalized canonical correlation is a research topic. Over the lifetime, 117 publications have been published within this topic receiving 5622 citations.


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Journal ArticleDOI
TL;DR: A general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text and compares orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model is presented.
Abstract: We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation and enables a comparison between the text and images. In the experiments, we look at two approaches of retrieving images based on only their content from a text query. We compare orthogonalization approaches against a standard cross-representation retrieval technique known as the generalized vector space model.

3,051 citations

Journal ArticleDOI
TL;DR: In this paper, five extensions of the classical two-set theory of canonical correlation analysis to three or more sets are considered, and a model of the general principal component type is constructed to aid in motivating, comparing and understanding the methods.
Abstract: SUMMARY Five extensions of the classical two-set theory of canonical correlation analysis to three or more sets are considered. For each one, a model of the general principal component type is constructed to aid in motivating, comparing and understanding the methods. Procedures are developed for finding the canonical variables associated with the different approaches. Some practical considerations and an example are also included.

781 citations

Journal ArticleDOI
TL;DR: Regularized generalized canonical correlation analysis (RGCCA) as mentioned in this paper combines the power of multi-block data analysis methods (maximization of well identified criteria) and the flexibility of PLS path modeling (the researcher decides which blocks are connected and which are not).
Abstract: Regularized generalized canonical correlation analysis (RGCCA) is a generalization of regularized canonical correlation analysis to three or more sets of variables. It constitutes a general framework for many multi-block data analysis methods. It combines the power of multi-block data analysis methods (maximization of well identified criteria) and the flexibility of PLS path modeling (the researcher decides which blocks are connected and which are not). Searching for a fixed point of the stationary equations related to RGCCA, a new monotonically convergent algorithm, very similar to the PLS algorithm proposed by Herman Wold, is obtained. Finally, a practical example is discussed.

290 citations

Journal ArticleDOI
TL;DR: An FD technique combining the generalized CCA with the threshold-setting based on the randomized algorithm is proposed and applied to the simulated traction drive control system of high-speed trains and shows that the proposed method is able to improve the detection performance significantly in comparison with the standard generalized C CA-based FD method.
Abstract: In this paper, we first study a generalized canonical correlation analysis (CCA)-based fault detection (FD) method aiming at maximizing the fault detectability under an acceptable false alarm rate. More specifically, two residual signals are generated for detecting of faults in input and output subspaces, respectively. The minimum covariances of the two residual signals are achieved by taking the correlation between input and output into account. Considering the limited application scope of the generalized CCA due to the Gaussian assumption on the process noises, an FD technique combining the generalized CCA with the threshold-setting based on the randomized algorithm is proposed and applied to the simulated traction drive control system of high-speed trains. The achieved results show that the proposed method is able to improve the detection performance significantly in comparison with the standard generalized CCA-based FD method.

252 citations

Journal ArticleDOI
TL;DR: In this article, the problem of determining linear functions for two sets of variables so as to maximize the correlation between the two functions has been solved by Hotelling, and a more efficient computational solution for the case of two sets is presented for any number of sets.
Abstract: The problem of determining linear functions for two sets of variables so as to maximize the correlation between the two functions has been solved by Hotelling. This article presents a more efficient computational solution for the case of two sets of variables and a generalized solution for any number of sets. Applications are discussed and a numerical example is included to demonstrate the solution for more than two sets.

190 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20219
202012
201912
20188
20175
20164