Qifa Ke

TL;DR: This paper forms matrix factorization as a L/sub 1/ norm minimization problem that is solved efficiently by alternative convex programming that is robust without requiring initial weighting, handles missing data straightforwardly, and provides a framework in which constraints and prior knowledge can be conveniently incorporated.

TL;DR: This paper starts with canonical correlation analysis (CCA), a popular and successful approach for mapping visual and textual features to the same latent space, and incorporates a third view capturing high-level image semantics, represented either by a single category or multiple non-mutually-exclusive concepts.

TL;DR: This paper presents a novel scheme where image features are bundled into local groups and each group of bundled features becomes much more discriminative than a single feature, and within each group simple and robust geometric constraints can be efficiently enforced.

TL;DR: This paper optimization product quantization by minimizing quantization distortions w.r.t. the space decomposition and the quantization codebooks and presents two novel methods for optimization: a non-parametric method that alternatively solves two smaller sub-problems, and a parametric method guarantees the optimal solution if the input data follows some Gaussian distribution.

TL;DR: This paper optimize PQ by minimizing quantization distortions w.r.t the space decomposition and the quantization codebooks, and evaluates the optimized product quantizers in three applications: compact encoding for exhaustive ranking, inverted multi-indexing for non-exhaustive search, and compacting image representations for image retrieval.