Effective dimensionality reduction in multimedia applications

In multimedia information retrieval, multimedia data such as images and videos are represented as vectors in high-dimensional space. To search these vectors efficiently, a variety of indexing methods have been proposed. However, the performance of these indexing methods degrades dramatically with increasing dimensionality, which is known as the dimensionality curse. To resolve the dimensionality curse, dimensionality reduction methods have been proposed. They map feature vectors in high-dimensional space into vectors in low-dimensional space before the data are indexed. This paper proposes an improvement for the previously proposed dimensionality reduction. The previous method uses the norm and the approximated angle for every subvector. However, more storage space and a number of cosine computations are required because of multiple angle components. In this paper, we propose an alternative method employing a single angle component instead of respective angles for all the subvectors. Because only one angle for every subvector is considered, though the loss of information regarding the original data vector increases, which degrades the performance slightly, we can successfully reduce storage space as well as a number of cosine computations. Finally, we verify the superiority of the proposed approach via extensive experiments with synthetic and real-life data sets.