Fast update of latent semantic spaces using a linear transform framework

Latent semantic analysis suffers from a relatively high sensitivity to both task domain and composition style. Because the traditional “folding-in” process simply populates the existing semantic vector space with current data, performance degrades when training and operating conditions differ. On the other hand, recomputing the semantic space from scratch typically precludes real-time operation. An adaptation strategy therefore makes sense as a potential compromise. This paper exploits a linear transform framework to update the semantic space as new data becomes available. Leveraging suitable Cholesky factorizations makes the update computationally efficient. Experiments with different increment sizes are conducted, and the paper discusses the comparative merits of this approach under several scenarios.