» Multi-task Vector Field Learning
Advances in Neural Information Processing Systems 25 (NIPS), 2012.
» Image Compression by Learning to Minimize the Total Error
IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), to appear.
Image compression by storing the gray-scale image and some seeds of color pixels. Semi-supervised regression is used to recover the remaining colors. Active learning algorithm that minimize the recover error is proposed to select optimal color seeds. The use of encoded difference-image further improve the colorization quality drastically.
Advances in Neural Information Processing Systems 24 (NIPS), 2011.
We use parallel vector field on the manifold to regularize (the gradient field of) a function so that it would be as "linear" as possible on the manifold.
» A Variance Minimization Criterion to Feature Selection using Laplacian Regularization pdf
IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), 2011.
Feature selection by minimizing the variance of the learned model parameters (similar objective function to optimal experimental design). A Laplacian regularizer is added to the model to let it adapt to the underlay geometry of the data manifold.
Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2010.
We proposed a simple unsupervised feature selection algorithm for multi-cluster data. The features are selected to optimally preserve the multi-cluster structure of the data. The optimization problem is solved via a sparse eigen-problem and a $\ell_1$-regularized least square problem.
» Parallel Vector Field Embedding
Journal of Machine Learning Research (JMLR), under review.
» A-Optimal Projection for Image Representation
IEEE Transaction on Pattern Analysis and Machine Intelligence (TPAMI), under review.