Semi-supervised learning with kernel locality-constrained linear coding

Abstract

Semi-supervised learning uses both labeled and unlabeled data for machine learning tasks. It's especially useful in the scenarios where labeled data is very scarce or expensive to obtain. In this work, we present kernel LLC, the kernel locality-constrained linear coding within a data-dependent kernel space, for data representation. The data-dependent kernel captures the underlying data geometry on the ambient feature space. The kernel LLC further exploits the locality association among the data on its manifold. Promising results on both image classification and content-based image retrieval scenarios suggest kernel LLC to be a good candidate for data representation in semi-supervised learning.