Semi-supervised learning

Learning Semi-Supervised Representation Towards a Unified Optimization Framework for Semi-Supervised Learning

We formulated the existing two-stage SSL problem into a unified optimization framework – termed as Self-Taught Semi-Supervised Learning (STSSL), in which both the given labels and the estimated labels are incorporated to refine the affinity matrix and to facilitate the unknown label estimation.

Regularized Semi-Supervised Latent Dirichlet Allocation for visual concept learning

We propose a novel regularized Semi-Supervised Latent Dirichlet Allocation (r-SSLDA) for learning visual concept classifiers.

Fixed-Rank Representation for Unsupervised Visual Learning

This paper proposed a novel framework, named fixedrank representation (FRR), for robust unsupervised visual learning.

Non-Negative Low Rank and Sparse Graph for Semi-Supervised Learning

This paper proposes a novel informative graph, called the nonnegative low rank and sparse graph (NNLRS-graph), for graph-based semi-supervised learning.