Kernel Canonical correlation analysis (KCCA) is a fundamental method with broad applicability in statistics and machine learning. Although there exist closedform solution to the KCCA objective by solving an N × N eigenvalue system where N is the training set size, the computational requirements of this approach in both memory and time prohibit its usage in the large scale. Various approximation...

Knowledge of the relationship between model parameters and forecast quantities is useful because it can aid in setting the values of the former for the purpose of having a desired effect on the latter. Here it is proposed that a well-establishedmultivariate statistical method known as canonical correlation analysis can be formulated to gauge the strength of that relationship. Themethod is appli...

1. Canonical correlation analysis (CCA) is a technique to extract common features from a pair of multivariate data. In complex situations, however, it does not extract useful features because of its linearity. On the other hand, kernel method used in support vector machine (Vapnik, 1998) is an efficient approach to improve such a linear method. In this study, we investigate the effectiveness of...

We give a probabilistic interpretation of canonical correlation (CCA) analysis as a latent variable model for two Gaussian random vectors. Our interpretation is similar to the probabilistic interpretation of principal component analysis (Tipping and Bishop, 1999, Roweis, 1998). In addition, we can interpret Fisher linear discriminant analysis (LDA) as CCA between appropriately defined vectors.

Two new methods for dealing with missing values in generalized canonical correlation analysis are introduced. The first approach, which does not require iterations, is a generalization of the Test Equating method available for principal component analysis. In the second approach, missing values are imputed in such a way that the generalized canonical correlation analysis objective function does...

0167-8655/$ see front matter 2010 Elsevier B.V. A doi:10.1016/j.patrec.2010.09.025 q The work of O. Kursun was supported by Scienti nation Unit of Istanbul University under the grant YA ⇑ Corresponding author. Tel.: +90 212 473 7070/17 E-mail addresses: [email protected] (O. Kurs Alpaydin), [email protected] (O.V. Favorov). Fisher’s linear discriminant analysis (LDA) is one of the most ...

Canonical correlation analysis (CCA) is a classical multivariate method concerned with describing linear dependencies between sets of variables. After a short exposition of the linear sample CCA problem and its analytical solution, the article proceeds with a detailed characterization of its geometry. Projection operators are used to illustrate the relations between canonical vectors and variat...

In the multi-view regression problem, we have a regression problem where the input variable (which is a real vector) can be partitioned into two different views, where it is assumed that either view of the input is sufficient to make accurate predictions — this is essentially (a significantly weaker version of) the co-training assumption for the regression problem. We provide a semi-supervised ...

Recently, multi-view feature extraction has attracted great interest and Canonical Correlation Analysis (CCA) is a powerful technique for finding the linear correlation between two view variable sets. However, CCA does not consider the structure and cross view information in feature extraction, which is very important for subsequence tasks. In this paper, a new approach called Canonical Sparse ...

We review a new method of performing Canonical Correlation Analysis (CCA) with Artificial Neural Networks. We have previously [5, 4] compared its capabilities with standard statistical methods on simple data sets where the maximum correlations are given by linear filters. In this paper, we extend the method by implementing a very precise set of constraints which allow multiple correlations to b...