We propose an image annotation framework which sequentially performs tag completion and refinement
We formulate the tag completion problem in a subspace clustering model which assumes that images are sampled from subspaces, and complete the tags using the state-of-the-art Low Rank Representation (LRR) method.
We proposed a new retraction for the quotient manifold used in R3MC, which is a geometric optimization algorithm for the matrix completion problem.
We propose linearized alternating direction method with parallel splitting and adaptive penalty for efficiently solving linearly constrained multi-variable separable convex programs, which are abundant in machine learning.