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THE STATISTICAL SIGNIFICANCE OF CANONICAL CORRELATIONS
Biometrika, 1941Not ...
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Multivariate Behavioral Research, 1975
A Monte Carlo study was run to check the stability of canonical correlations, canonical weights, and canonical variate-variable correlations. Eight data matrices were selected from the literature for the canonical analyses, with the number of variables ranging from 7 to 41.
R S, Barcikowski, J P, Stevens
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A Monte Carlo study was run to check the stability of canonical correlations, canonical weights, and canonical variate-variable correlations. Eight data matrices were selected from the literature for the canonical analyses, with the number of variables ranging from 7 to 41.
R S, Barcikowski, J P, Stevens
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Canonical correlations and canonical variates
1985In this chapter we shall summarize the essential elements of the theory of canonical correlations and variates. We shall begin by formulating the problem. The derivation of canonical correlations and canonical variates will then be taken up. Canonical analysis can be derived in several ways.
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Parametric Canonical Correlation Analysis
2019 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), 2019Generally, suppose a wave is a linear combination of multiple basis(Not necessarily a sine or cosine waves, it could also be a wavelet, etc.), different types of waves may be similar on some basis, but vary greatly on a certain basis. To address this problem, we introduce a PCCA-based feature extraction method that extends canonical correlation ...
Shangyu Chen +2 more
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Canonical random correlation analysis
Proceedings of the 2010 ACM Symposium on Applied Computing, 2010Canonical correlation analysis (CCA) is one of the most well-known methods to extract features from multi-view data and has attracted much attention in recent years. However, classical CCA is unsupervised and does not take class label information into account.
Jianchun Zhang, Daoqiang Zhang
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Generalised Canonical Correlation Analysis
2000Canonical Correlation Analysis [3] is used when we have two data sets which we believe have some underlying correlation. In this paper, we derive a new family of neural methods for finding the canonical correlation directions by solving a generalized eigenvalue problem. Based on the differential equation for the generalized eigenvalue problem, a family
Zhenkun Gou, Colin Fyfe
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On canonical correlation and redundancy
九州大学大学院総合理工学報告, 1988Ke, Hui-xin, Asano, Choichiro
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Discriminative Multiple Canonical Correlation Analysis for Information Fusion
IEEE Transactions on Image Processing, 2018Lin Qi, Enqing Chen, Ling Guan
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