Probabilistic latent semantic analysis

About: Probabilistic latent semantic analysis is a research topic. Over the lifetime, 2884 publications have been published within this topic receiving 198341 citations. The topic is also known as: PLSA.

Combining evidence for effective information filtering

Abstract: As part of NLqT/ARPA’s TREC Wcckshop, we used Semamk Indexing (LSl) for filtering 33~ incoming dotemere from diveae ~ (aew~es, I~ttenta tedmiotl ~mractO for SO t~i~ of ~ We develot~edrepmeematiom of user intems~ ~ mtea, for these topics ~ two mu~ces of emmi~ infmmalion. A Won/F/~r used just the weeds in the ~#c mmnems, and a Re/Do~ F//~r u~d just ̄ e known n/ram t~s do~.me~ and ~ ~e ~#c ,memm. U~ the mlevam mdnin8 documents (a variant of relevance &edback) was mote effective than ~ ̄ detailed natural ’-,,-mage description of in~ts. Coral/nins these two vec~n provided some ~ iml~ovemmm in mmdn~ On averap, 7 ~ the top 10 docmnmes ~md 44 of the top 1(]0 ~ ~ mlev ̄llt nslno the combined ~ metlwd. Dam coml/nation ’~n~_ the results of the Wa/d and RelDoca reuieval .-ts was not generally succe,dul in ~ pe~onm~e ~oqmed m the best individual method, thoNlh we believe it might be if additional murces me used. These ~ mmtmds me quite gen. end and applicable to ̄ variety of muting aad feedback

Abnormal event detection and localisation in traffic videos based on group sparse topical coding

Abstract: In visual surveillance, detecting and localising abnormal events are of great interest. In this study, an unsupervised method is proposed to automatically discover abnormal events occurring in traffic videos. For learning typical motion patterns occurring in such videos, a group sparse topical coding (GSTC) framework and an improved version of it are applied to optical flow features extracted from video clips. Then a very simple and efficient algorithm is proposed for GSTC. It is shown that discovered motion patterns can be employed directly in detecting abnormal events. A variety of abnormality metrics based on the resulting sparse codes for detection of abnormality are investigated. Experiments show that the result of the approach in detection and localisation of abnormal events is promising. In comparison with other usual methods (probabilistic latent semantic analysis, latent Dirichlet allocation, sparse topical coding (STC) and improved STC), according to the values of area under ROC, the proposed method achieves at least 14% improvement in abnormal event detection.

An extension of PLSA for document clustering

Abstract: In this paper we propose an extension of the PLSA model in which an extra latent variable allows the model to co-cluster documents and terms simultaneously. We show on three datasets that our extended model produces statistically significant improvements with respect to two clustering measures over the original PLSA and the multinomial mixture MM models.

Co-regularized PLSA for multi-view clustering

Abstract: Multi-view data is common in a wide variety of application domains. Properly exploiting the relations among different views is helpful to alleviate the difficulty of a learning problem of interest. To this end, we propose an extended Probabilistic Latent Semantic Analysis (PLSA) model for multi-view clustering, named Co-regularized PLSA (CoPLSA). CoPLSA integrates individual PLSAs in different views by pairwise co-regularization. The central idea behind the co-regularization is that the sample similarities in the topic space from one view should agree with those from another view. An EM-based scheme is employed for parameter estimation, and a local optimal solution is obtained through an iterative process. Extensive experiments are conducted on three real-world datasets and the compared results demonstrate the superiority of our approach.