4 accepted papers
Multiview representation learning techniques based on deep correlation maximization have become increasingly popular for learning meaningful and compact representations from multiview data. Even though their performance is state-of-the-art in many interpretability-critical fields, their black-box be…
Deep correlation-based multiview representation learning techniques have become increasingly popular methods for extracting highly correlated representations from multiview data. However, their ability to find highly complex mappings between the views can also lead to overfitting and overly correlat…
Data fusion—the joint analysis of multiple datasets—through coupled factorizations has the promise to enable enhanced knowledge discovery, and hence is an active area. Various formulations of coupled matrix factorizations have been proposed, each with its own modeling assumptions. In this paper, we…
Estimating the number of correlated components between two data sets is a challenging task in the case of small sample support. Typically, a rank-reduction preprocessing step based on principal component analysis (PCA) is carried out on each data set individually to reduce the dimensionality before…