Group $K$-Means
Abstract
We study how to learn multiple dictionaries from a dataset, and approximate any data point by the sum of the codewords each chosen from the corresponding dictionary. Although theoretically low approximation errors can be achieved by the global solution, an effective solution has not been well studied in practice. To solve the problem, we propose a simple yet effective algorithm \textit{Group -Means}. Specifically, we take each dictionary, or any two selected dictionaries, as a group of -means cluster centers, and then deal with the approximation issue by minimizing the approximation errors. Besides, we propose a hierarchical initialization for such a non-convex problem. Experimental results well validate the effectiveness of the approach.
Cite
@article{arxiv.1501.00825,
title = {Group $K$-Means},
author = {Jianfeng Wang and Shuicheng Yan and Yi Yang and Mohan S Kankanhalli and Shipeng Li and Jingdong Wang},
journal= {arXiv preprint arXiv:1501.00825},
year = {2015}
}
Comments
The developed algorithm is similar with "Christopher F. Barnes, A new multiple path search technique for residual vector quantizers, 1994", but we conduct the research independently and apply it in data/feature compression and image retrieval