Gaussian mixture distance for information retrieval
Abstract
We propose a Gaussian mixture distance for performing accurate nearest-neighbor search for information retrieval. Under an established Gaussian finite mixture model for the distribution of the data in the database, the Gaussian mixture distance is formulated based on minimizing the Kullback-Leibler divergence between the distribution of the retrieval data and the data in database. We compared the performance of the Gaussian mixture distance with the well-known Euclidean and Mahalanobis distance based on a precision performance measurement. Experimental results demonstrate that the Gaussian mixture distance function is superior in the others for different types of testing data.
Authors: Xiaoqing Li, Irwin King
Published in: International Joint Conference on Neural Networks (IJCNN) (2003)