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.

Integrating Intra-Speaker Topic Modeling and Temporal-Based Inter-Speaker Topic Modeling in Random Walk for Improved Multi-Party Meeting Summarization.

Abstract: This paper proposes an improved approach of summarization for spoken multi-party interaction, in which intra-speaker and inter-speaker topics are modeled in a graph constructed with topical relations. Each utterance is represented as a node of the graph and the edge between two nodes is weighted by the similarity between the two utterances, which is topical similarity evaluated by probabilistic latent semantic analysis (PLSA). We model intra-speaker topics by sharing the topics from the same speaker and inter-speaker topics by partially sharing the topics from the adjacent utterances based on temporal information. We did experiments for ASR and manual transcripts. For both types of transcripts, experiments confirmed the efficacy of combining intraand inter-speaker topic modeling for summarization.

Continuous human action recognition in real time

Abstract: This paper discusses the task of continuous human action recognition. By continuous, it refers to videos that contain multiple actions which are connected together. This task is important to applications like video surveillance and content based video retrieval. It aims to identify the action category and detect the start and end key frame of each action. It is a challenging task due to the frequent changes of human actions and the ambiguity of action boundaries. In this paper, a novel and efficient continuous action recognition framework is proposed. Our approach is based on the bag of words representation. A visual local pattern is regarded as a word and the action is modeled by the distribution of words. A generative translation and scale invariant probabilistic Latent Semantic Analysis model is presented. The continuous action recognition result is obtained frame by frame and updated from time to time. Experimental results show that this approach is effective and efficient to recognize both isolated actions and continuous actions.

A web recommendation technique based on probabilistic latent semantic analysis

Abstract: Web transaction data between Web visitors and Web functionalities usually convey user task-oriented behavior pattern. Mining such type of click-stream data will lead to capture usage pattern information. Nowadays Web usage mining technique has become one of most widely used methods for Web recommendation, which customizes Web content to user-preferred style. Traditional techniques of Web usage mining, such as Web user session or Web page clustering, association rule and frequent navigational path mining can only discover usage pattern explicitly. They, however, cannot reveal the underlying navigational activities and idntity the latent relationships that are associated with the patterns among Web users as well as Web pages. In this work, we propose a Web recommendation framework incorporating Web usage mining technique based on Probabilistic Latent Semantic Analysis (PLSA) model. The main advantages of this method are, not only to discover usage-based access pattern, but also to reveal the underlying latent factor as well. With the discovered user access pattern, we then present user more interested content via collaborative recommendation. To validate the effectiveness of proposed approach, we conduct experiments on real world datasets and make comparisons with some existing traditional techniques. The preliminary experimental results demonstrate the usability of the proposed approach.

Diverse Topic Phrase Extraction through Latent Semantic Analysis

Abstract: We propose a novel algorithm for extracting diverse topic phrases in order to provide summary for large corpora. Previous works often ignore the importance of diversity and thus extract phrases crowded on some hot topics while failing to cover other less obvious but important topics. We solve this problem through document re-weighting and phrase diversification by using latent semantic analysis (LSA). Experiments on various datasets show that our new algorithm can improve relevance as well as diversity over different topics for topic phrase extraction problems.

An image inpainting method using pLSA-based search space estimation

Abstract: In this paper, we present a novel exemplar-based image inpainting technique based on the local context measure of the target patch Three main steps of the proposed method are determination of patch priority, the search space estimation for the candidate patches and the patch completion to fill in the unknown pixels of the target patch In patch priority, we emphasize on the structure by the spatial relationship of neighborhood similar patches and kernel regression based local image structure We find the search space, sub-regions of the entire source region similar to the region surrounding the target patch, to find the candidate patches The said search space is estimated using probabilistic latent semantic analysis (pLSA) Last, we infer the unknown pixels of the target patch using pLSA-based context and histogram similarity measure between the target patch and the candidate patches Experimental results are found to be good compared to the competitive methods and may be used for digital restoration of images of defective or damaged artifacts