Dimensionality Reduction with Latent Variable Model

Over the past few decades, latent variable model (LVM)-based algorithms have attracted considerable attention for the purpose of data dimensionality reduction, which plays an important role in machine learning, pattern recognition, and computer vision. LVM is an effective tool for modeling density of the observed data. It has been used in dimensionality reduction for dealing with the sparse observed samples. In this paper, two LVM-based dimensionality reduction algorithms are presented firstly, i.e., supervised Gaussian process latent variable model and semi-supervised Gaussian process latent variable model. Then, we propose an LVM-based transfer learning model to cope with the case that samples are not independent identically distributed. In the end of each part, experimental results are given to demonstrate the validity of the proposed dimensionality reduction algorithms.