Dimensionality Reduction and Similarity Computation by Inner Product Approximations

—As databases increasingly integrate different types of information such as multimedia, spatial, time-series, and scientific data, it becomes necessary to support efficient retrieval of multidimensional data. Both the dimensionality and the amount of data that needs to be processed are increasing rapidly. Reducing the dimension of the feature vectors to enhance the performance of the underlying technique is a popular solution to the infamous curse of dimensionality. We expect the techniques to have good quality of distance measures when the similarity distance between two feature vectors is approximated by some notion of distance between two lower-dimensional transformed vectors. Thus, it is desirable to develop techniques resulting in accurate approximations to the original similarity distance. In this paper, we investigate dimensionality reduction techniques that directly target minimizing the errors made in the approximations. In particular, we develop dynamic techniques for effic...

• Type: Conference
• Year: 2000
• Where: CIKM
• Authors: Ömer Egecioglu, Hakan Ferhatosmanoglu
• Keywords: Accurate Approximation, CIKM 2000, Feature Vectors, Information Management, Similarity Distance