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modeling Location-Based User Rating Profiles for Personalized Recommendation

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2015年第3期9卷 1-41页

作者： Yin, Hongzhi Cui, Bin Chen, Ling Hu, Zhiting Zhang, Chengqi Univ Queensland Sch Informat Technol & Elect Engn Brisbane Qld 4072 Australia Peking Univ Sch EECS Key Lab High Confidence Software Technol MOE Beijing 100871 Peoples R China Univ Technol Sydney QCIS Sydney NSW 2007 Australia Carnegie Mellon Univ Language Technol Inst Pittsburgh PA 15213 USA

This article proposes LA-LDA, a location-aware probabilistic generative model that exploits location-based ratings to model user profiles and produce recommendations. Most of the existing recommendation models do not consider the spatial information of users or items;however, LA-LDA supports three classes of location-based ratings, namely spatial user ratings for nonspatial items, nonspatial user ratings for spatial items, and spatial user ratings for spatial items. LA-LDA consists of two components, ULA-LDA and ILA-LDA, which are designed to take into account user and item location information, respectively. The component ULA-LDA explicitly incorporates and quantifies the influence from local public preferences to produce recommendations by considering user home locations, whereas the component ILA-LDA recommends items that are closer in both taste and travel distance to the querying users by capturing item co-occurrence patterns, as well as item location co-occurrence patterns. The two components of LA-LDA can be applied either separately or collectively, depending on the available types of location-based ratings. To demonstrate the applicability and flexibility of the LA-LDA model, we deploy it to both top-k recommendation and cold start recommendation scenarios. Experimental evidence on large-scale real-world data, including the data from Gowalla (a location-based social network), DoubanEvent (an event-based social network), and MovieLens (a movie recommendation system), reveal that LA-LDA models user profiles more accurately by outperforming existing recommendation models for top-k recommendation and the cold start problem.

关键词： Algorithms Design Experimentation Performance User profile recommender system cold start probabilistic generative model location-based services

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FusionNet: An Unsupervised Convolutional Variational Network for Hyperspectral and Multispectral Image Fusion

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2020年 29卷 7565-7577页

作者： Wang, Zhengjue Chen, Bo Lu, Ruiying Zhang, Hao Liu, Hongwei Varshney, Pramod K. Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Natl Lab Radar Signal Proc Xian 710071 Peoples R China Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

Due to hardware limitations of the imaging sensors, it is challenging to acquire images of high resolution in both spatial and spectral domains. Fusing a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI) to obtain an HR-HSI in an unsupervised manner has drawn considerable attention. Though effective, most existing fusion methods are limited due to the use of linear parametric modeling for the spectral mixture process, and even the deep learning-based methods only focus on deterministic fully-connected networks without exploiting the spatial correlation and local spectral structures of the images. In this paper, we propose a novel variational probabilistic autoencoder framework implemented by convolutional neural networks, in order to fuse the spatial and spectral information contained in the LR-HSI and HR-MSI, called FusionNet. The FusionNet consists of a spectral generative network, a spatial-dependent prior network, and a spatial-spectral variational inference network, which are jointly optimized in an unsupervised manner, leading to an end-to-end fusion system. Further, for fast adaptation to different observation scenes, we give a meta-learning explanation to the fusion problem, and combine the FusionNet with meta-learning in a synergistic manner. Effectiveness and efficiency of the proposed method are evaluated based on several publicly available datasets, demonstrating that the proposed FusionNet outperforms the state-of-the-art fusion methods.

关键词： Hyperspectral images multispectral images image fusion probabilistic generative model convolutional neural network meta-learning

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Statistical Inference of Diffusion Networks

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2021年第2期33卷 742-753页

作者： Huang, Hao Yan, Qian Chen, Lu Gao, Yunjun Jensen, Christian S. Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China Aalborg Univ Dept Comp Sci DK-9220 Aalborg Denmark Zhejiang Univ Coll Comp Sci & Technol Hangzhou 310027 Peoples R China

To infer structures in diffusion networks, existing approaches mostly need to know not only the final infection statuses of network nodes, but also the exact times when infections occur. In contrast, in many real-world settings, such as disease propagation, monitoring exact infection times is often infeasible due to a high cost. We investigate the problem of how to learn diffusion network structures based on only the final infection statuses of nodes. Instead of utilizing sequences of timestamps to determine potential parent-child influence relationships between nodes, we propose to find influence relationships with high statistical significance. To this end, we design a probabilistic generative model of the final infection statuses to quantitatively measure the likelihood of potential structures of the objective diffusion network, taking into account network complexity. Based on this model, we can infer an appropriate number of most probable parent nodes for each node in the network. Furthermore, to reduce redundant inference computations, we are able to preclude insignificant candidate parent nodes from being considered during inferencing, if their infections have little correlation with the infections of the corresponding child nodes. Extensive experiments on both synthetic and real-world networks offer evidence that the proposed approach is effective and efficient.

关键词： Diffusion network influence relationship infection timestamp probabilistic generative model

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Active exploration based on information gain by particle filter for efficient spatial concept formation

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ADVANCED ROBOTICS 2023年第13期37卷 840-870页

作者： Taniguchi, Akira Tabuchi, Yoshiki Ishikawa, Tomochika El Hafi, Lotfi Hagiwara, Yoshinobu Taniguchi, Tadahiro Ritsumeikan Univ Coll Informat Sci & Engn Kusatsu Shiga Japan Ritsumeikan Univ Grad Sch Informat Sci & Engn Kusatsu Shiga Japan Ritsumeikan Univ Res Org Sci & Technol Kusatsu Shiga Japan

Autonomous robots need to learn the categories of various places by exploring their environments and interacting with users. However, preparing training datasets with linguistic instructions from users is time-consuming and labor-intensive. Moreover, effective exploration is essential for appropriate concept formation and rapid environmental coverage. To address this issue, we propose an active inference method, referred to as spatial concept formation with information gain-based active exploration (SpCoAE) that combines sequential Bayesian inference using particle filters and information gain-based destination determination in a probabilistic generative model. This study interprets the robot's action as a selection of destinations to ask the user, 'What kind of place is this?' in the context of active inference. This study provides insights into the technical aspects of the proposed method, including active perception and exploration by the robot, and how the method can enable mobile robots to learn spatial concepts through active exploration. Our experiment demonstrated the effectiveness of the SpCoAE in efficiently determining a destination for learning appropriate spatial concepts in home environments.

关键词： Active exploration active inference probabilistic generative model particle filtering spatial concept

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Label Correlation Mixture model: A Supervised generative Approach to Multilabel Spoken Document Categorization

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2015年第2期3卷 235-245页

作者： He, Zhiyang Wu, Ji Li, Tao Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China Florida Int Univ Sch Comp Sci Miami FL 33199 USA

Multilabel categorization, which is more difficult but practical than the conventional binary and multiclass categorization, has received a great deal of attention in recent years. This paper proposes a novel probabilistic generative model, label correlation mixture model (LCMM), to depict the multiply labeled documents, which can be used for multilabel spoken document categorization as well as multilabel text categorization. In LCMM, labels and topics have the one-to-one correspondences. The LCMM consists of two important components: 1) a label correlation model and 2) a multilabel conditioned document model. The label correlation model formulates the generating process of labels where the dependences between the labels are taken into account. We also propose an efficient algorithm for calculating the probability of generating an arbitrary subset of labels. The multilabel conditioned document model can be regarded as a supervised label mixture model, in which labels for a document are known. Each label is characterized by distributions over words. For the parameter learning of the multilabel conditioned document model, in addition to maximum likelihood estimation, a discriminative approach based on the minimum classification error rate training is proposed. To evaluate LCMM, extensive multilabel categorization experiments are conducted on a spoken document data set and three standard text data sets. The experimental results in comparison with other competitive methods demonstrate the effectiveness of LCMM.

关键词： Label correlation mixture model probabilistic generative model multi-label spoken document categorization multi-label text categorization Bayesian decision theory minimum classification error rate method

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Towards complex activity recognition using a Bayesian network-based probabilistic generative framework

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PATTERN RECOGNITION 2017年 68卷 295-309页

作者： Liu, Li Wang, Shu Su, Guoxin Huang, Zi-Gang Liu, Ming Minist Educ Key Lab Dependable Serv Comp Cyber Phys Soc Chongqing 400044 Peoples R China Chongqing Univ Sch Software Engn Chongqing 400044 Peoples R China Southwest Univ Fac Mat & Energy Chongqing 400715 Peoples R China Univ Wollongong Sch Comp & Informat Technol Wollongong NSW 2522 Australia Lanzhou Univ Sch Phys Sci & Technol Lanzhou 730000 Peoples R China Southwest Univ Fac Comp & Informat Sci Chongqing 400715 Peoples R China

Complex activity recognition is challenging since a complex activity can be performed in different ways, with each having its own configuration of primitive events and their temporal dependencies. To address such temporal relational variabilities in complex activity recognition, we propose a Bayesian network based probabilistic generative framework that employs Allen's interval relation network to represent local temporal dependencies in a generative way. By employing the Chinese restaurant process and introducing relation generation constraints, our framework can characterize these unique internal configurations of a particular complex activity as a joint distribution. Three concrete models are implemented based on our framework. Specifically, in this paper we improve two of our previous models and provide an enhanced model to handle temporal relational variabilities in complex activities more efficiently. Empirical evaluations on three benchmark datasets demonstrate the competitiveness of our framework. In particular, it is shown that our models are rather robust against errors caused by the low-level predictions from raw signals. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Activity recognition Bayesian network Complex activity probabilistic generative model Temporal relation Uncertainty

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Unsupervised Multimodal Word Discovery Based on Double Articulation Analysis With Co-Occurrence Cues

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 2023年第4期15卷 1825-1840页

作者： Taniguchi, Akira Murakami, Hiroaki Ozaki, Ryo Taniguchi, Tadahiro Ritsumeikan Univ Coll Informat Sci & Engn Kusatsu Shiga 5258577 Japan Ritsumeikan Univ Grad Sch Informat Sci & Engn Kusatsu Shiga 5258577 Japan

Human infants acquire their verbal lexicon with minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other sensory stimuli. This study proposes a novel fully unsupervised learning method for discovering speech units using phonological information as a distributional cue and object information as a co-occurrence cue. The proposed method can acquire words and phonemes from speech signals using unsupervised learning and utilize object information based on multiple modalities-vision, tactile, and auditory-simultaneously. The proposed method is based on the nonparametric Bayesian double articulation analyzer (NPB-DAA) discovering phonemes and words from phonological features, and multimodal latent Dirichlet allocation (MLDA) categorizing multimodal information obtained from objects. In an experiment, the proposed method showed higher word discovery performance than baseline methods. Words that expressed the characteristics of objects (i.e., words corresponding to nouns and adjectives) were segmented accurately. Furthermore, we examined how learning performance is affected by differences in the importance of linguistic information. Increasing the weight of the word modality further improved performance relative to that of the fixed condition.

关键词： Robot sensing systems Computational modeling Unsupervised learning Statistical learning Linguistics probabilistic logic Bayes methods Co-occurrence cues developmental robotics lexical acquisition probabilistic generative model word discovery

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A tutorial on Bayesian models of perception

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JOURNAL OF MATHEMATICAL PSYCHOLOGY 2015年 66卷 103-114页

作者： Vincent, Benjamin T. Univ Dundee Sch Psychol Dundee DD1 4HN Scotland

The notion that perception involves Bayesian inference is an increasingly popular position taken by many researchers. Bayesian models have provided insights into many perceptual phenomena, but their description and practical implementation does not always convey their theoretical appeal or conceptual elegance. This tutorial provides an introduction to core concepts in Bayesian modelling and should help a wide variety of readers to more deeply understand, or to generate their own Bayesian models of perception. Core theoretical and implementational issues are covered, using the 2 alternative-forced-choice task as a case study. Supplementary code is available to help bridge the gap between model description and practical implementation (see Appendix B). (C) 2015 Elsevier Inc. All rights reserved.

关键词： Bayesian network Bayesian inference Alternative forced choice MCMC Ideal observer Psychometric function probabilistic generative model

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An overview of topic modeling and its current applications in bioinformatics

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SPRINGERPLUS 2016年第1期5卷 1608页

作者： Liu, Lin Tang, Lin Dong, Wen Yao, Shaowen Zhou, Wei Yunnan Univ Sch Informat Kunming 650091 Yunnan Peoples R China Yunnan Normal Univ Key Lab Educ Informatizat Nationalities Minist Educ Sch Informat Kunming 650092 Yunnan Peoples R China Yunnan Normal Univ Minist Educ Key Lab Educ Informatizat Nationalities Kunming 650092 Yunnan Peoples R China Yunnan Univ Natl Pilot Sch Software Kunming 650091 Yunnan Peoples R China

Background: With the rapid accumulation of biological datasets, machine learning methods designed to automate data analysis are urgently needed. In recent years, socalled topic models that originated from the field of natural language processing have been receiving much attention in bioinformatics because of their interpretability. Our aim was to review the application and development of topic models for bioinformatics. Description: This paper starts with the description of a topic model, with a focus on the understanding of topic modeling. A general outline is provided on how to build an application in a topic model and how to develop a topic model. Meanwhile, the literature on application of topic models to biological data was searched and analyzed in depth. According to the types of models and the analogy between the concept of document-topic-word and a biological object (as well as the tasks of a topic model), we categorized the related studies and provided an outlook on the use of topic models for the development of bioinformatics applications. Conclusion: Topic modeling is a useful method (in contrast to the traditional means of data reduction in bioinformatics) and enhances researchers' ability to interpret biological information. Nevertheless, due to the lack of topic models optimized for specific biological data, the studies on topic modeling in biological data still have a long and challenging road ahead. We believe that topic models are a promising method for various applications in bioinformatics research.

关键词： Topic model Bioinformatics probabilistic generative model Clustering Classification

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MGMR: leveraging RNA-Seq population data to optimize expression estimation

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BMC BIOINFORMATICS 2012年第s6期13卷 1-7页

作者： Rozov, Roye Halperin, Eran Shamir, Ron Tel Aviv Univ Blavatnik Sch Comp Sci IL-69978 Tel Aviv Israel Tel Aviv Univ Mol Microbiol & Biotechnol Dept IL-69978 Tel Aviv Israel Int Comp Sci Inst Berkeley CA 94704 USA

Background: RNA-Seq is a technique that uses Next Generation Sequencing to identify transcripts and estimate transcription levels. When applying this technique for quantification, one must contend with reads that align to multiple positions in the genome (multireads). Previous efforts to resolve multireads have shown that RNA-Seq expression estimation can be improved using probabilistic allocation of reads to genes. These methods use a probabilistic generative model for data generation and resolve ambiguity using likelihood-based approaches. In many instances, RNA-seq experiments are performed in the context of a population. The generative models of current methods do not take into account such population information, and it is an open question whether this information can improve quantification of the individual samples Results: In order to explore the contribution of population level information in RNA-seq quantification, we apply a hierarchical probabilistic generative model, which assumes that expression levels of different individuals are sampled from a Dirichlet distribution with parameters specific to the population, and reads are sampled from the distribution of expression levels. We introduce an optimization procedure for the estimation of the model parameters, and use HapMap data and simulated data to demonstrate that the model yields a significant improvement in the accuracy of expression levels of paralogous genes. Conclusions: We provide a proof of principal of the benefit of drawing on population commonalities to estimate expression. The results of our experiments demonstrate this approach can be beneficial, primarily for estimation at the gene level.

关键词： Paralogous Gene Dirichlet Distribution Expression Estimate probabilistic generative model Homo Sapiens

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