Efficient Data Analytics on Augmented Similarity Triplets

Data analysis requires a pairwise proximity measure over objects. Recent work has extended this to situations where the distance information between objects is given as comparison results of distances between three objects (triplets). Humans find comparison tasks much easier than the exact distance computation, and such data can be easily obtained in big quantities via crowdsourcing. In this work, we propose triplets augmentation, an efficient method to extend the triplets data by inferring the hidden implicit information from the existing data. Triplets augmentation improves the quality of kernel-based and kernel-free data analytics. We also propose a novel set of algorithms for common data analysis tasks based on triplets. These methods work directly with triplets and avoid kernel evaluations, thus are scalable to big data. We demonstrate that our methods outperform the current best-known techniques and are robust to noisy data.