CSVD: Clustering and singular value decomposition for approximate similarity search in high-dimensional spaces

Abstract

Nearest-neighbor search of high-dimensionality spaces is critical for many applications, such as content-based retrieval from multimedia databases, similarity search of patterns in data mining, and nearest-neighbor classification. Unfortunately, even with the aid of the commonly used indexing schemes, the performance of nearest-neighbor (NN) queries deteriorates rapidly with the number of dimensions. We propose a method, called Clustering with Singular Value Decomposition (CSVD), which supports efficient approximate processing of NN queries, while maintaining good precision-recall characteristics. CSVD groups homogeneous points into clusters and separately reduces the dimensionality of each cluster using SVD. Cluster selection for NN queries relies on a branch-and-bound algorithm and within-cluster searches can be performed with traditional or in-memory indexing methods. Experiments with texture vectors extracted from satellite images show that CSVD achieves significantly higher dimensionality reduction than plain SVD for the same Normalized Mean Squared Error (NMSE), which translates into a higher efficiency in processing approximate NN queries.