We consider the task of dimensionality reduction for regression (DRR) whose goal is to find a low dimensional representation of input covariates, while preserving the statistical ...
We propose a non-linear Canonical Correlation Analysis (CCA) method which works by coordinating or aligning mixtures of linear models. In the same way that CCA extends the idea of...
Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and indicate important underlying structure...
This paper presents a new approach to feature analysis in automatic speech recognition (ASR) based on locality preserving projections (LPP). LPP is a manifold based dimensionality...
—A novel non-linear dimensionality reduction method, called Temporal Laplacian Eigenmaps, is introduced to process efficiently time series data. In this embedded-based approach,...
Michal Lewandowski, Jesus Martinez-Del-Rincon, Dim...