Probabilistic relational PCA (PRPCA) can learn a projection matrix to perform dimensionality reduction for relational data. However, the results learned by PRPCA lack interpretabi...
We present a generative model for performing sparse probabilistic projections, which includes sparse principal component analysis and sparse canonical correlation analysis as spec...
The sparse error correction is intimately related to Compressive Sensing. Exploiting this connection, the paper proposes an error correction scheme pivoted on partial Fourier matr...
Many noise models do not faithfully reflect the noise processes introduced during data collection in many real-world applications. In particular, we argue that a type of noise re...
Object tracking is one of the fundamental problems in computer vision and has received considerable attention in the past two decades. The success of a tracking algorithm relies on...