Vector approximation based indexing for non-uniform high dimensional data sets

With the proliferation of multimedia data, there is increasing need to support the indexing and searching of high dimensional data. Recently, a vector approximation based technique called VAle has been proposed for indexing high dimensional data. It has been shown that the VAle is an e ective technique compared to the current approaches based on space and data partitioning. The VAle gives good performance especially when the data set is uniformly distributed. Real data sets are not uniformly distributed, are often clustered, and the dimensions of the feature vectors in real data sets are usually correlated. More careful analysis for nonuniform or correlated data is needed for e ectively indexing high dimensional data. We propose a solution to these problems and propose the VAle, a new technique for indexing high dimensional data sets based on vector approximations. We conclude with an evaluation of nearest neighbor queries and show that the VAle technique results in signi cant improvements over the current VAle approach for several real data sets.