Sequential EM learning for subspace analysis

Image Classification via Sparse Representation and Subspace Alignment

Image representation is a crucial problem in image processing where there exist many low-level representations of image, i.e., SIFT, HOG and so on. But there is a missing link across low-level and high-level semantic representations. In fact, traditional machine learning approaches, e.g., non-negative matrix factorization, sparse representation and principle component analysis are employed to d...

Large Scale Bayesian Kernel Learning with the Subspace EM Algorithm

Recurrent least square learning for quasi-parallel principal component analysis

The recurrent least squares (RLS) learning approach is proposed for controlling the learning rate in parallel principal subspace analysis (PSA) and in a wide class of principal component analysis (PCA) associated algorithms with a quasi{parallel extraction ability. The purpose is to provide a useful tool for applications where the learning process has to be repeated in an on{line self{adaptive ...

Nonlinear Discriminant Analysis Using Kernel Functions

Fishers linear discriminant analysis (LDA) is a classical multivariate technique both for dimension reduction and classification. The data vectors are transformed into a low dimensional subspace such that the class centroids are spread out as much as possible. In this subspace LDA works as a simple prototype classifier with linear decision boundaries. However, in many applications the linear bo...

Mining Actionable Subspace Clusters in Sequential Data

Extraction of knowledge from data and using it for decision making is vital in various real-world problems, particularly in the financial domain. We identify several financial problems, which require the mining of actionable subspaces defined by objects and attributes over a sequence of time. These subspaces are actionable in the sense that they have the ability to suggest profitable action for...