Over the past few decades, latent variable model （LVM）-based algorithms have attracted consid- erable attention for the purpose of data diInensional- ity reduction, which plays an important role in machine learning,...Over the past few decades, latent variable model （LVM）-based algorithms have attracted consid- erable attention for the purpose of data diInensional- ity 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 la- tent variable model and senti-supervised Gaussian pro- cess 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.展开更多
文摘Over the past few decades, latent variable model （LVM）-based algorithms have attracted consid- erable attention for the purpose of data diInensional- ity 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 la- tent variable model and senti-supervised Gaussian pro- cess 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.
