We propose a channel-correlation adaptive dictionary learning based demosaicking (CADLD).
We propose a novel dictionary learning with structured noise (DLSN) method for handling noisy data. We decompose the original data into three parts: clean data, structured noise, and Gaussian noise, and then characterize them separately.
We present a novel joint dictionary learning and semantic constrained latent subspace learning method for cross-modal retrieval (JDSLC).
We present a novel bilevel model-based discriminative dictionary learning method for recognition tasks.
We propose a tensor low-rank representation (TLRR) and sparse coding-based (TLRRSC) subspace clustering method by simultaneously considering feature information and spatial structures.
In this work we propose the low-rank representation (LRR) to recover the lowest-rank representation of a set of data vectors in a joint way, i.e., to recover the lowest-rank representation of matrix data.