This paper aims at constructing a good graph to discover the intrinsic data structures under a semisupervised learning setting.
We propose a novel multi-level discriminative dictionary learning method and apply it to large scale image classification.
We regard geodesic distance as a kind of kernel, which maps data from linearly inseparable space to linear separable distance space. In doing this, a new semisupervised manifold learning algorithm, namely regularized geodesic feature learning algorithm, is proposed.
With the L1-norm discriminant criterion, we propose a new linear discriminant analysis (L1-LDA) method for linear feature extraction problem.
We have proposed a new multi-manifold learning algorithm, which combines semi-supervised multimanifold modeling, nonlinear feature extraction and a new semi-supervised discriminant analysis method to achieve better performance in geodesic feature extraction and classification tasks.
We propose a new discriminant subspace analysis (DSA) method for the multi-class feature extraction problems.
We propose a novel algorithm, called Semisupervised Semi-Riemannian Metric Map (S^3RMM), following the geometric framework of semi- Riemannian manifolds
We develop a supervised dimensionality reduction method, called Lorentzian discriminant projection(LDP), for feature extraction and classification
We propose the linear Laplacian discrimination (LLD) algorithm/or discriminant feature extraction, which is an extension of linear discriminant analysis (LDA). Our motivation is to address the issue that LDA cannot work well in cases where sample spaces are non-Euclidean.