The concept of probabilistic Latent Semantic Analysis (pLSA) has gained much interest as a tool for feature transformation in image categorization and scene recognition scenarios. However, a major issue of this technique is overfitting. Therefore, we propose to use an ensemble of pLSA models which are trained using random fractions of the training data. We analyze empirically the influence of t...

This paper addresses the issue of identifying persons with expertise knowledge on a given topic. Traditional methods usually estimate the relevance between the query and the support documents of candidate experts using, for example, a language model. However, the language model lacks the ability of identifying semantic knowledge, thus results in some right experts cannot be found due to not occ...

In this paper, we present a novel Probabilistic Latent Semantic Analysis-based (PLSA-based) aspect model and turn cross-media retrieval into two parts of multi-modal integration and correlation propagation. We first use multivariate Gaussian distributions to model continuous quantity in PLSA, avoiding information loss between feature-instance versus real-world matching. Multi-modal correlations...

Web users use search engine to find useful information on the Internet. However current web search engines return answer to a query independent of specific user information need. Since web users with similar web behaviors tend to acquire similar information when they submit a same query, these unseen factors can be used to improve search result. In this paper we present an approach that mines t...

Historically, many believe that these three papers [7, 8, 9] established the techniques of Probabilistic Latent Semantic Analysis or PLSA for short. However, there also exists one variant of the model in [11] and indeed all these models were originally discussed in an earlier technical report [10]. In [2], the authors extended MLE-style estimation of PLSA to MAP-style estimations. A hierarchica...

A dense sensor network consisting of passive infrared motion detectors was developed and used to record human activity in hallways and rooms in a large campus building. Algorithms were developed that: (a) automatically determine the topology of the network from the sensor data, so that manual mapping is not required, (b) automatically learn patterns of sensor readings in local spatial and tempo...

This paper presents a strategy to generate generic summary of documents using Probabilistic Latent Semantic Indexing. Generally a document contains several topics rather than a single one. Summaries created by human beings tend to cover several topics to give the readers an overall idea about the original document. Hence we can expect that a summary containing sentences from better part of the ...

In this paper, we present an approach to learning latent semantic analysis models from loosely annotated images for automatic image annotation and indexing. The given annotation in training images is loose due to: (1) ambiguous correspondences between visual features and annotated keywords; (2) incomplete lists of annotated keywords. The second reason motivates us to enrich the incomplete annot...

This paper presents a model-based approach to spoken document similarity called Supervised Probabilistic Latent Semantic Analysis (PLSA). The method differs from traditional spoken document similarity techniques in that it allows similarity to be learned rather than approximated. The ability to learn similarity is desirable in applications such as Internet video recommendation, in which complex...

Abstract Document collections evolve over time, new topics emerge and old ones decline. At the same time, the terminology evolves as well. Much literature is devoted to topic evolution in nite document sequences assuming a xed vocabulary. In this study, we propose \Topic Monitor" for the monitoring and understanding of topic and vocabulary evolution over an in nite document sequence, i.e. a str...