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Online belief propagation algorithm for probabilistic latent semantic analysis

Frontiers of Computer Science 2013年第4期7卷 526-535页

作者： Yun YE Shengrong GONG Chunping LIU Jia ZENG Ning JIA YiZHANG School of Computer Science & Technology Soochow University Suzhou 215006 China Feng Chao Revenue Baidu Online Network Technology Co. LTD Beijing 100000 China

probabilistic latent semantic analysis （PLSA） is a topic model for text documents, which has been widely used in text mining, computer vision, computational biology and so on. For batch PLSA inference algorithms, the required memory size grows linearly with the data size, and handling massive data streams is very difficult. To process big data streams, we propose an online belief propagation （OBP） algorithm based on the improved factor graph representation for PLSA. The factor graph of PLSA facilitates the classic belief propagation （BP） algorithm. Furthermore, OBP splits the data stream into a set of small segments, and uses the estimated parameters of previous segments to calculate the gradient descent of the current segment. Because OBP removes each segment from memory after processing, it is memoryefficient for big data streams. We examine the performance of OBP on four document data sets, and demonstrate that OBP is competitive in both speed and accuracy for online ex- pectation maximization （OEM） in PLSA, and can also give a more accurate topic evolution. Experiments on massive data streams from Baidu further confirm the effectiveness of the OBP algorithm.

关键词： probabilistic latent semantic analysis topicmodels expectation maximization belief propagation

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Re-examining urban region and inferring regional function based on spatial-temporal interaction

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International Journal of Digital Earth 2019年第3期12卷 293-310页

作者： Haiyan Tao Keli Wang Li Zhuo Xuliang Li Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and PlanningSun Yat-sen UniversityGuangzhouPeople’s Republic of China Center of Integrated Geographic Information analysis School of Geography and PlanningSun Yat-sen UniversityGuangzhouPeople’s Republic of China Key Laboratory of Tropical Disease Control(Sun Yat-sen University) Ministry of EducationGuangzhouPeople’s Republic of China

Urban system is shaped by the interactions between different regions and regions planned by the government,then reshaped by human activities and residents’*** the changes of regional structure and dynamics of city function based on the residents’movement demand are important to evaluate and adjust the planning and management of urban services and internal *** paper constructed a probabilistic factor model on the basis of probabilistic latent semantic analysis and tensor decomposition,for purpose of understanding the higher order interactive population mobility and its impact on urban structure ***,a four-dimensional tensor of time(T)×week(W)×origin(O)×destination(D)was constructed to identify the day-to-day activities in three time modes and weekly regularity of weekday/weekend *** we reclassified the urban regions based on the space clustering formed by the space factor matrix and core ***,we further analysed the space–time interaction on different time scales to deduce the actual function and connection strength of each *** research shows that the application of individual-based spatial–temporal data in human mobility and space–time interaction study can help to analyse urban spatial structure and understand the actual regional function from a new perspective.

关键词： Tensor decomposition probabilistic latent semantic analysis taxi space–time administrative district

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Beyond bag of latent topics: spatial pyramid matching for scene category recognition

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Frontiers of Information Technology & Electronic Engineering 2015年第10期16卷 817-828页

作者： Fu-xiang LU Jun HUANG School of Information Science & Engineering Lanzhou University Shanghai Advanced Research Institute Chinese Academy of Sciences

We propose a heterogeneous, mid-level feature based method for recognizing natural scene categories. The proposed feature introduces spatial information among the latent topics by means of spatial pyramid, while the latent topics are obtained by using probabilistic latent semantic analysis （pLSA） based on the bag-of-words representation. The proposed feature always performs better than standard pLSA because the performance of pLSA is adversely affected in many cases due to the loss of spatial information. By combining various interest point detectors and local region descriptors used in the bag-of-words model, the proposed feature can make further improvement for diverse scene category recognition tasks. We also propose a two-stage framework for multi-class classification. In the first stage, for each of possible detector/descriptor pairs, adaptive boosting classifiers are employed to select the most discriminative topics and further compute posterior probabilities of an unknown image from those selected topics. The second stage uses the prod-max rule to combine information coming from multiple sources and assigns the unknown image to the scene category with the highest ＇final＇ posterior probability. Experimental results on three benchmark scene datasets show that the proposed method exceeds most state-of-the-art methods.

关键词： Scene category recognition probabilistic latent semantic analysis Bag-of-words Adaptive boosting

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CTMIR:A Novel Correlated Topic Model for Image Retrieval

CTMIR:A Novel Correlated Topic Model for Image Retrieval

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2009 Second International Workshop on Knowledge Discovery and Data Mining

作者： Jian Wen TAO College of Information Engineering Zhejiang Business Technology Institute Ningbo City China Pei Fen DING College of Information Engineering Zhejiang Business Technology Institute Ningbo City China

＜正＞Representation of images by the latent Dirichlet Allocation model combined with an appropriate similarity measure is suitable for performing large scale image retrieval in a real-world *** LDA model,however,relies on the assumption that all topics are independent of each other something that is obviously not true in most *** this work we study a recently proposed model,the Correlated Topic Model（CTM）[1],in the context of large-scale image retrieval. This approach is able to explicitly model such correlations of *** experimentally evaluate the proposed retrieval approach on a real-world large-scale database consisting of more than 246,000 images and compare the performance to related approaches.

关键词： probabilistic latent semantic analysis latent Dirichlet Allocation Image Retrieval Similarity Measure

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