Skip to Main Content
The structures of feature vectors based semi-supervised/supervised learning has gained considerable interests in the past several years thanks to its effectiveness for better object modeling and classication. In many machine learning and computer vision tasks, a critical issue is the similarity between two feature vectors. In this paper, we present a novel technique to measure the similarities among feature vectors by decomposing each feature vector as an `1 sparse linear combination of the rest of the feature vectors. The main idea is that the coefcients in such a sparse decomposition reect the features' neighborhood structure thus providing better similarity measures among the decomposed feature vector and the rest of the feature vectors. The proposed approach is applied to label propagation and action recognition, and is evaluated on several commonly-used data sets. The experimental results show that the proposed Sparsity Induced Similarity (SIS) measure signicantly improves the performance of both label propagation and action recognition.