Scalable similarity search with optimized kernel hashing

Scalable similarity search is the core of many large scale learning or data mining applications. Recently, many research results demonstrate that one promising approach is creating compact and efficient hash codes that preserve data similarity. By efficient, we refer to the low correlation (and thus low redundancy) among generated codes. However, most existing hash methods are designed only for vector data. In this paper, we develop a new hashing algorithm to create efficient codes for large scale data of general formats with any kernel function, including kernels on vectors, graphs, sequences, sets and so on. Starting with the idea analogous to spectral hashing, novel formulations and solutions are proposed such that a kernel based hash function can be explicitly represented and optimized, and directly applied to compute compact hash codes for new samples of general formats. Moreover, we incorporate efficient techniques, such as Nystr¨om approximation, to further reduce time and spa...

• Type: Journal
• Year: 2010
• Where: KDD
• Authors: Junfeng He, Wei Liu, Shih-Fu Chang
• Keywords: Data Mining, General Formats, Hash Codes, KDD 2010, Large Scale