Submanifold-Preserving Discriminant Analysis With an Auto-Optimized Graph.

Due to the multimodality of non-Gaussian data, traditional globality-preserved dimensionality reduction (DR) methods, such as linear discriminant analysis (LDA) and principal component analysis (PCA) are difficult to deal with. In this paper, we present a novel local DR framework via auto-optimized graph embedding to extract the intrinsic submanifold structure of multimodal data. Specifically, the proposed model seeks to learn an embedding space which can preserve the local neighborhood structure by constructing a k -nearest neighbors ( k NNs) graph on data points. Different than previous works, our model employs the l