Canonical correlation analysis is a family of multivariate statistical methods for the analysis
of paired sets of variables. Since its proposition, canonical correlation analysis has, for …

Canonical correlation analysis (CCA) is a popular and powerful dimensionality reduction
method to analyze paired multi-view data. However, when facing semi-paired and semi …

Due to the noise disturbance and limited number of training samples, within-set and
between-set sample covariance matrices in canonical correlation analysis (CCA) usually …

For dimension reduction on multiview data, most of the previous studies implicitly take an
assumption that all samples are completed in all views. Nevertheless, this assumption could …

Domain adaptation learning is one of the fundamental research topics in pattern recognition
and machine learning. This paper introduces a regularized semipaired kernel canonical …

The recent advent of stacked memory devices has led to a resurgence of research
associated with the fundamental memory hierarchy and associated memory pipeline. The …

Multiview clustering explores complementary information among distinct views to enhance
clustering performance under the assumption that all samples have complete information in …

This paper presents a multi-modal Gaussian process latent variable model with semi-
supervised label dequantization. In real-world applications, although user ratings are often …

Multiset canonical correlation analysis (MCCA) aims at revealing the linear correlations
among multiple sets of high-dimensional data. Therefore, it is only a linear multiview …

Canonical correlation analysis (CCA) is a famous data analysis method that has been
successfully used in many areas. CCA extracts meaningful information from a pair of data …