Online Learning for PLSA-Based Visual Recognition

Jie Xu, Getian Ye, Yang Wang, Wei Wang, Jun Yang

Abstract: Probabilistic Latent Semantic Analysis (PLSA) is one of the latent topic models and it has been successfully applied to visual recognition tasks. However, PLSA models have been learned mainly in batch learning, which can not handle data that arrives sequentially. In this paper, we propose a novel on-line learning algorithm for learning the parameters of PLSA. Our contributions are two-fold: (i) an on-line learning algorithm that learns the parameters of a PLSA model from incoming data; (ii) a codebook adaptation algorithm that can capture the full characteristics of all the features during the learning. Experimental results demonstrate that the proposed algorithm can handle sequentially arriving data that batch PLSA learning cannot cope with, and its performance is comparable with that of the batch PLSA learning on visual recognition.