Partial-update dimensionality reduction for accumulating co-occurrence events

Abstract

The paper addresses a novel problem when learning similarities. In our problem, an input is given by a long sequence of co-occurrence events among objects, namely a stream of co-occurrence events. Given a stream of co-occurrence events, we learn unknown latent vectors of objects such that their inner product adaptively approximates the target similarities resulting from accumulating co-occurrence events. Toward this end, we propose a new incremental algorithm for dimensionality reduction. The core of our algorithm is its partial updating style where only a small number of latent vectors are modified for each co-occurrence event, while most other latent vectors remain unchanged. Experiment results using both synthetic and real data sets demonstrate that in contrast to some existing methods, the proposed algorithm can stably and gradually learn target similarities among objects without being trapped by the collapsing problem. (C) 2013 Elsevier B.V. All rights reserved.