As increasing amounts of sensitive personal information is aggregated into data repositories, it has become important to develop mechanisms for processing the data without revealin...
We present a novel probabilistic latent variable model to perform linear dimensionality reduction on data sets which contain clusters. We prove that the maximum likelihood solution...
Subspace learning based face recognition methods have attracted considerable interests in recently years, including Principal Component Analysis (PCA), Linear Discriminant Analysi...
Deng Cai, Xiaofei He, Yuxiao Hu, Jiawei Han, Thoma...
A linear, discriminative, supervised technique for reducing feature vectors extracted from image data to a lower-dimensional representation is proposed. It is derived from classica...
This paper presents a novel pattern recognition framework by capitalizing on dimensionality increasing techniques. In particular, the framework integrates Gabor image representatio...