We study the problem of discovering a manifold that best preserves information relevant to a nonlinear regression. Solving this problem involves extending and uniting two threads ...
Given a classification problem, our goal is to find a low-dimensional linear transformation of the feature vectors which retains information needed to predict the class labels. We...
Independent Component Analysis is becoming a popular exploratory method for analysing complex data such as that from FMRI experiments. The application of such `model-free' me...
Linear Discriminant Analysis (LDA) has been a popular method for feature extracting and face recognition. As a supervised method, it requires manually labeled samples for training...
In this paper, a novel subspace learning method, semi-supervised marginal discriminant analysis (SMDA), is proposed for classification. SMDA aims at maintaining the intrinsic neig...