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Multi-modal Bayesian embedding for point-of-interest recommendation on location-based cyber-physical-social networks

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 108卷 1119-1128页

作者： Huang, Liwei Ma, Yutao Liu, Yanbo Sangaiah, Arun Kumar Beijing Inst Remote Sensing Beijing 100854 Peoples R China Wuhan Univ State Key Lab Software Engn Wuhan 430072 Peoples R China VIT Univ Sch Comp Sci & Engn Vellore 632014 Tamil Nadu India

Point-of-interest (POI) recommendation has become a favorite topic on location-based cyber-physicalsocial networks (LBCPSNs). The geographical influence of locations (e.g., geographical proximity) and the social influence of friends (e.g., social ties) have been widely used in many existing POI recommendation methods, most of which paid little attention to the sequential influence and temporal influence of locations on users' check-in behaviors. However, human mobility exhibits sequential and temporal patterns. In this paper, we propose a unified probabilistic generative model, i.e., a multi-modal Bayesian embedding model (MMBE), to discover the social, sequential, temporal, and spatial patterns of users' check-in behaviors simultaneously. Besides, MMBE can model the joint effect of the four factors mentioned above on the decision-making for POI selection across heterogeneous workflow activities in cyber-physicalsocial (CPS) systems. Then, we conduct an in-depth performance evaluation for MMBE using two largescale real-world datasets from Gowalla and Brightkite. Experimental results show that our proposed method outperforms the other five state-of-the-art POI recommendation approaches regarding Precision and Recall. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Point-of-interest recommendation Location-based social networks Multi-modal information probabilistic generative model Cyber-physical-social systems

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Training and compensation of class-conditioned NMF bases for speech enhancement

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NEUROCOMPUTING 2018年 284卷 107-118页

作者： Chung, Hanwook Badeau, Roland Plourde, Eric Champagne, Benoit McGill Univ Dept Elect & Comp Engn Montreal PQ Canada Univ Paris Saclay Telecom ParisTech LTCI F-75013 Paris France Sherbrooke Univ Dept Elect & Comp Engn Sherbrooke PQ Canada

In this paper, we introduce a training and compensation algorithm of the class-conditioned basis vectors in the non-negative matrix factorization (NMF) model for single-channel speech enhancement. The main goal is to estimate the basis vectors of different signal sources in a way that prevents them from representing each other, in order to reduce the residual noise components that have features similar to the speech signal. During the proposed training stage, the basis matrices for the clean speech and noises are estimated jointly by constraining them to belong to different classes. To this end, we employ the probabilistic generative model (PGM) of classification, specified by class-conditional densities, as an a priori distribution for the basis vectors. The update rules of the NMF and the PGM parameters of classification are jointly obtained by using the variational Bayesian expectation-maximization (VBEM) algorithm, which guarantees convergence to a stationary point. Another goal of the proposed algorithm is to handle a mismatch between the characteristics of the training and test data. This is accomplished during the proposed enhancement stage, where we implement a basis compensation scheme. Specifically, we use extra free basis vectors to capture the features that are not included in the training data. Objective experimental results for different combination of speaker and noise types show that the proposed algorithm can provide better speech enhancement performance than the benchmark algorithms under various conditions. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Single-channel speech enhancement Non-negative matrix factorization probabilistic generative model Classification Variational Bayesian expectation-maximization

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Exploring inconsistencies in genome-wide protein function annotations: a machine learning approach

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BMC BIOINFORMATICS 2007年第1期8卷 1-12页

作者： Andorf, Carson Dobbs, Drena Honavar, Vasant Iowa State Univ Dept Comp Sci Artificial Intelligence Lab Ames IA 50011 USA Iowa State Univ Dept Genet Dev & Cell Biol Ames IA 50011 USA Iowa State Univ Bioinformat & Computat Biol Grad Program Ames IA 50011 USA Iowa State Univ Ctr Computat Intelligence Learning & Discovery Ames IA 50011 USA

Background: Incorrectly annotated sequence data are becoming more commonplace as databases increasingly rely on automated techniques for annotation. Hence, there is an urgent need for computational methods for checking consistency of such annotations against independent sources of evidence and detecting potential annotation errors. We show how a machine learning approach designed to automatically predict a protein's Gene Ontology ( GO) functional class can be employed to identify potential gene annotation errors. Results: In a set of 211 previously annotated mouse protein kinases, we found that 201 of the GO annotations returned by AmiGO appear to be inconsistent with the UniProt functions assigned to their human counterparts. In contrast, 97% of the predicted annotations generated using a machine learning approach were consistent with the UniProt annotations of the human counterparts, as well as with available annotations for these mouse protein kinases in the Mouse Kinome database. Conclusion: We conjecture that most of our predicted annotations are, therefore, correct and suggest that the machine learning approach developed here could be routinely used to detect potential errors in GO annotations generated by high-throughput gene annotation projects.

关键词： Gene Ontology Class Label Machine Learning Approach Evidence Code probabilistic generative model

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Bayesian body schema estimation using tactile information obtained through coordinated random movements

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ADVANCED ROBOTICS 2017年第3期31卷 118-134页

作者： Mimura, Tomohiro Hagiwara, Yoshinobu Taniguchi, Tadahiro Inamura, Tetsunari Ritsumeikan Univ Grad Sch Sci & Engn Kusatsu Japan Natl Inst Informat Principles Informat Res Div Tokyo Japan Grad Univ Adv Studies Principles Informat Res Div Tokyo Japan

This paper describes a computational model, called the Dirichlet process Gaussian mixture model with latent joints (DPGMM-LJ), that can find latent tree structure embedded in data distribution in an unsupervised manner. By combining DPGMM-LJ and a preexisting body map formation method, we propose a method that enables an agent having multilink body structure to discover its kinematic structure, i.e. body schema, from tactile information alone. The DPGMM-LJ is a probabilistic model based on Bayesian nonparametrics and an extension of Dirichlet process Gaussian mixture model (DPGMM). In a simulation experiment, we used a simple fetus model that had five body parts and performed structured random movements in a womb-like environment. It was shown that the method could estimate the number of body parts and kinematic structures without any preexisting knowledge in many cases. Another experiment showed that the degree of motor coordination in random movements affects the result of body schema formation strongly. It is confirmed that the accuracy rate for body schema estimation had the highest value 84.6% when the ratio of motor coordination was 0.9 in our setting. These results suggest that kinematic structure can be estimated from tactile information obtained by a fetus moving randomly in a womb without any visual information, even though its accuracy was not so high. They also suggest that a certain degree of motor coordination in random movements and the sufficient dimension of state space that represents the body map are important to estimate body schema correctly.

关键词： Body schema body map probabilistic generative model Bayesian nonparametrics general movements

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Necessary conditions for STDP-based pattern recognition learning in a memristive spiking neural network

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NEURAL NETWORKS 2021年 134卷 64-75页

作者： Demin, V. A. Nekhaev, D., V Surazhevsky, I. A. Nikiruy, K. E. Emelyanov, A., V Nikolaev, S. N. Rylkov, V. V. Kovalchuk, M., V Kurchatov Inst Natl Res Ctr Moscow Russia Moscow Inst Phys & Technol Dolgoprudnyi Russia Kotelnikov Inst Radio Engn & Elect RAS Fryazino 141190 Moscow Region Russia Lomonosov Moscow State Univ Moscow Russia

This work is aimed to study experimental and theoretical approaches for searching effective local training rules for unsupervised pattern recognition by high-performance memristor-based Spiking Neural Networks (SNNs). First, the possibility of weight change using Spike-Timing-Dependent Plasticity (STDP) is demonstrated with a pair of hardware analog neurons connected through a (CoFeB)(x)(LiNbO3)(1-x) nanocomposite memristor. Next, the learning convergence to a solution of binary clusterization task is analyzed in a wide range of memristive STDP parameters for a single-layer fully connected feedforward SNN. The memristive STDP behavior supplying convergence in this simple task is shown also to provide it in the handwritten digit recognition domain by the more complex SNN architecture with a Winner-Take-All competition between neurons. To investigate basic conditions necessary for training convergence, an original probabilistic generative model of a rate-based single layer network with independent or competing neurons is built and thoroughly analyzed. The main result is a statement of "correlation growth-anticorrelation decay"principle which prompts near optimal policy to configure model parameters. This principle is in line with requiring the binary clusterization convergence which can be defined as the necessary condition for optimal learning and used as the simple benchmark for tuning parameters of various neural network realizations with population-rate information coding. At last, a heuristic algorithm is described to experimentally find out the convergence conditions in a memristive SNN, including robustness to a device variability. Due to the generality of the proposed approach, it can be applied to a wide range of memristors and neurons of softwareor hardware-based rate-coding single-layer SNNs when searching for local rules that ensure their unsupervised learning convergence in a pattern recognition task domain. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Memristor Hardware analog neuron Spiking neural network Memristive STDP Unsupervised learning probabilistic generative model

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CVTM: A Content-Venue-Aware Topic model for Group Event Recommendation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2020年第7期32卷 1290-1303页

作者： Du, Yulu Meng, Xiangwu Zhang, Yujie Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Beijing 100876 Peoples R China

Event recommendation is essential to help people find attractive events to attend, but it intrinsically faces cold-start problem. The previous studies exploit multiple contextual factors to overcome the cold-start problem in event recommendation. However, they do not consider the correlation among different contextual factors. Moreover, suggesting events for a group of users also has not been well studied. In this paper, we first discover the correlation between organizer and textual content, i.e., the events held by the same organizer tend to have more similar content. Based on this observation, we present a content-venue-aware topic model (CVTM) to capture group interests on an event from two perspectives: content and venue. The correlation between organizer and content is modeled in CVTM to alleviate the sparsity of textual content, and then we can further extract group interests on content of an event more accurately. Finally, a group event recommendation method using CVTM is proposed. We conduct comprehensive experiments to evaluate the recommendation performance of our model on two real-world datasets. The results demonstrate that the proposed model outperforms the state-of-the-art methods that suggest upcoming events for groups. Besides, CVTM can learn semantically coherent latent topics which are useful to explain recommendations.

关键词： Correlation Games Context modeling Social networking (online) probabilistic logic Recommender systems Task analysis Group recommendation probabilistic generative model event recommendation event-based social networks

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Exploiting Category-Level Multiple Characteristics for POI Recommendation

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第2期35卷 1488-1501页

作者： Dong, Zheng Meng, Xiangwu Zhang, Yujie Beijing Univ Posts & Telecommun Beijing Key Lab Intelligent Telecommun Software & Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing 100876 Peoples R China

Point of interest (POI) recommendation has received significant attention in recent years, most existing studies exploit multiple auxiliary information to alleviate the problem of data sparsity, and consider the sequential features of user mobility. However, few studies consider the category-level characteristics derived from each user's historical check-in frequencies, and the characteristics between different latent factors. In this paper, we exploit category-level multiple characteristics to generate recommendation. First, we obtain the frequency characteristics by in-depth analysis of the historical check-in frequencies for different categories by each user, and propose a scheme including KL-divergence and text analysis algorithm. Then we propose a category-level sequential- and non-sequential influence-aware probabilistic generative model (CSNS), which models the characteristics (correlation and indeterminate decisiveness) between user latent behavior topics and latent sequence patterns. We design two stages to generate recommendations. In the first stage, CSNS and frequencies characteristics are exploited jointly to recommend the POI categories that users may visit. In the second stage, we depend on user profiles and poi features, and sort the candidate POI sets by combining the POI categories provided in the first stage. Comprehensive experiments on two real-world datasets demonstrate that our method outperforms the existing state-of-the-art POI recommendation models.

关键词： Image color analysis Correlation Time-frequency analysis probabilistic logic Text analysis Social networking (online) Software Point-of-interest recommendation probabilistic generative model category-level location-based social networks check-in frequencies

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probabilistic Weighted Copula Regression model With Adaptive Sample Selection Strategy for Complex Industrial Processes

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2020年第11期16卷 6972-6981页

作者： Zhou, Yang Ren, Xiang Li, Shaojun East China Univ Sci & Technol Minist Educ Key Lab Adv Control & Optimizat Chem Proc Shanghai 200237 Peoples R China Rutgers State Univ Dept Chem & Biochem Engn Piscataway NJ 08854 USA

With complicated structures and complex physicochemical reactions, most industrial processes are intrinsically characterized by high dimensionality, nonlinearity, non-Gaussianity, and self-correlation. In this article, a novel probabilistic generative model, called weighted copula regression (WCR), is developed for complex processes. This method employs a probabilistic graphical tool (vine copula) to flexibly handle the underlying patterns via the factorization of complex dependence structures into multiple bivariate copulas. Monte Carlo estimation is incorporated for establishing a fast and reliable computing framework. To avoid overinterpretation of the industrial data, an adaptive sample selection strategy is proposed to explore the underlying distribution of the sample space and to select the most "informative" samples. By considering the copula weights that carry crucial information on the local data, the probabilistic WCR method can provide fast point estimate with prediction uncertainty for every predicted sample. The proposed WCR method is compared with six state-of-the-art methods, and the efficiency is validated using a numerical example and the ethylene cracking furnace process.

关键词： probabilistic logic Estimation Training Adaptation models Data models Probability density function Distribution functions Active learning industrial process Monte Carlo estimation probabilistic generative model probabilistic graphical tool soft sensor vine copula

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Heterogeneity-Aware Recurrent Neural Network for Hyperspectral and Multispectral Image Fusion

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IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING 2022年第4期16卷 649-665页

作者： Lu, Ruiying Chen, Bo Sun, Jianqiao Chen, Wenchao Wang, Penghui Chen, Yuanwei Liu, Hongwei Varshney, Pramod K. Xidian Univ Collaborat Innovat Ctr Informat Sensing & Underst Natl Lab Radar Signal Proc Xian 710071 Peoples R China China Acad Space Technol Beijing 100081 Peoples R China Syracuse Univ Dept Elect Engn & Comp Sci Syracuse NY 13244 USA

Due to the hardware limitations of remote imaging sensors, it is challenging to acquire images with high resolution in both the spatial and spectral domains. An effective and economical way to obtain high-resolution hyperspectral images (HR HSI) is to fuse low-resolution hyperspectral images (LR HSI) and high-resolution multispectral images (HR MSI). However, most existing deep learning based fusion methods employ the same network for all of the spectra without exploring their complex regional heterogeneity of hyperspectral characteristics. Taking various intrinsic spatial and spectral characteristics across different pixels into consideration, this paper proposes a mixture of recurrent neural networks (RNNs) under the variational probabilistic framework for spatial and spectral resolution enhancement. More specifically, we firstly cluster spectral characteristics into different groups, and employ different RNN experts for various spectra generation under the guidance of clustering. Moreover, a cluster-specific learnable Gaussian prior is proposed to provide a prior knowledge of heterogeneity. Further, an online variational inference scheme is derived for end-to-end optimization. Extensive experimental results demonstrate the effectiveness and efficiency of the proposed model on both synthetic and real datasets, compared with state-of-the-art unsupervised fusion methods.

关键词： Hyperspectral imaging Spatial resolution Tensors Bayes methods probabilistic logic Optimization Mathematical models Dirichlet process mixture model clustering recurrent neural network probabilistic generative model hyperspectral imaging image fusion

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A transfer learning approach using improved copula subspace division for multi-mode fault detection

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CANADIAN JOURNAL OF CHEMICAL ENGINEERING 2023年第12期101卷 7015-7030页

作者： Zhou, Yang Jia, Li Zhang, Yilan Shanghai Univ Sch Mechatron Engn & Automat Shanghai Peoples R China Southwest Inst Tech Phys Chengdu Peoples R China

Multi-mode characteristics of industrial processes are prominent in the area of chemical production due to a diversified market demand. Despite mounting interests in predictive modelling for the optimization of operating conditions in chemical production processes, particularly in the petrochemical industry with multiple feeds and a range of cracking furnaces, targeted solutions that could hold wider applicability are typically hindered by the lack of available data. To overcome the limitation posed by data scarcity, an inductive approach based on transfer learning for fault detection is proposed utilizing copula subspace division (CSD), named TrAdaBoost CSD (TCSD). The proposed TCSD method is based on the probability view to transfer different most similar source samples to target samples. To select the optimal number of source samples, two adaptive indices were proposed and designed to adaptively assign the optimal model training iterations and sample number per iteration. Imbalance in data samples between the target and source datasets was addressed via the adaptive active vine copula-based probabilistic method. The effectiveness and superiority of the proposed TCSD approaches are validated via a numerical example (with the ranges of normalized fault detection and false alarm rates [FDR and FAR] between 0.82-0.94 and 0.01-0.04, respectively), the Tennessee Eastman process (similar to 10% and 2.24% improvement for FDR and FAR, respectively), and the ethylene cracking furnace process (similar to 15% and 8% improvement for FDR and FAR, respectively).

关键词： imbalanced samples industrial process probabilistic generative model process monitoring TrAdaBoost

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