… a scalable latent topic model that we call “Sparse Latent Semantic Analysis” (Sparse LSA). …
select the most relevant words for each latent topic. There are several important features of …

… The data set for this excercise is, thus, too sparse for the term-weighting preprocessing to
have the optimal effect. Because of this, every word is treated as a content word, even content-…

… to the semantics encapsulated by the latent topic space. … probabilistic latent semantic
analysis (pLSA) [28] and latent … -depth sparse probabilistic latent semantic analysis (DEpLSA)…

… and consistent improvements over Latent Semantic Analysis in a number of experiments. …
are sparse, the corresponding low-dimensional latent vectors will typically not be sparse. This …

… LSA (originally known as Latent Semantic Indexing) was developed for the task of Information
… Instead, the currently used algorithms are optimized for dealing with sparse data spaces …

… ) of such structured sparse representation, our graph … of latent semantic learning in action
recognition. Unlike the traditional latent semantic analysis based on topic models, our latent …

… -negative Sparse Latent Semantic Analysis (NN-Sparse LSA) to discover the latent semantic
… the NN-Sparse LSA model. Furthermore, we implement the parallel version of our fast NN-…

… sparse, the corresponding low-dimensional latent vectors will typically not be … to latent
semantic analysis based on a statistical latent variable model, the general problem of text analysis …

… into a latent topic-… the semantics encapsulated by the latent topic space. Specifically, we
first define a pLSA-based extension, Sparse Multi-modal probabilistic Latent Semantic Analysis (…

… on sparse coding (SC),21 we propose an SC based probabilistic latent semantic analysis
(… Note that our method is diŪerent from some proposed sparse pLSA22 or sparse topic model…