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Randomized Latent Factor Model for High-dimensional and Sparse Matrices from Industrial Applications 被引量:13
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作者 Mingsheng Shang Xin Luo +3 位作者 Zhigang Liu Jia Chen Ye Yuan MengChu Zhou 《IEEE/CAA Journal of Automatica Sinica》 EI CSCD 2019年第1期131-141,共11页
Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts itera... Latent factor(LF) models are highly effective in extracting useful knowledge from High-Dimensional and Sparse(HiDS) matrices which are commonly seen in various industrial applications. An LF model usually adopts iterative optimizers,which may consume many iterations to achieve a local optima,resulting in considerable time cost. Hence, determining how to accelerate the training process for LF models has become a significant issue. To address this, this work proposes a randomized latent factor(RLF) model. It incorporates the principle of randomized learning techniques from neural networks into the LF analysis of HiDS matrices, thereby greatly alleviating computational burden. It also extends a standard learning process for randomized neural networks in context of LF analysis to make the resulting model represent an HiDS matrix correctly.Experimental results on three HiDS matrices from industrial applications demonstrate that compared with state-of-the-art LF models, RLF is able to achieve significantly higher computational efficiency and comparable prediction accuracy for missing data.I provides an important alternative approach to LF analysis of HiDS matrices, which is especially desired for industrial applications demanding highly efficient models. 展开更多
关键词 Big data high-dimensional and sparse matrix latent factor analysis latent factor model randomized learning
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Robust Latent Factor Analysis for Precise Representation of High-Dimensional and Sparse Data 被引量:5
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作者 Di Wu Xin Luo 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第4期796-805,共10页
High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurat... High-dimensional and sparse(HiDS)matrices commonly arise in various industrial applications,e.g.,recommender systems(RSs),social networks,and wireless sensor networks.Since they contain rich information,how to accurately represent them is of great significance.A latent factor(LF)model is one of the most popular and successful ways to address this issue.Current LF models mostly adopt L2-norm-oriented Loss to represent an HiDS matrix,i.e.,they sum the errors between observed data and predicted ones with L2-norm.Yet L2-norm is sensitive to outlier data.Unfortunately,outlier data usually exist in such matrices.For example,an HiDS matrix from RSs commonly contains many outlier ratings due to some heedless/malicious users.To address this issue,this work proposes a smooth L1-norm-oriented latent factor(SL-LF)model.Its main idea is to adopt smooth L1-norm rather than L2-norm to form its Loss,making it have both strong robustness and high accuracy in predicting the missing data of an HiDS matrix.Experimental results on eight HiDS matrices generated by industrial applications verify that the proposed SL-LF model not only is robust to the outlier data but also has significantly higher prediction accuracy than state-of-the-art models when they are used to predict the missing data of HiDS matrices. 展开更多
关键词 High-dimensional and sparse matrix L1-norm L2 norm latent factor model recommender system smooth L1-norm
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Aberrant activation of latent transforming growth factor-β initiates the onset of temporomandibular joint osteoarthritis 被引量:16
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作者 Liwei Zheng Caixia Pi +9 位作者 Jun Zhang Yi Fan Chen Cui Yang Zhou Jianxun Sun Quan Yuan Xin Xu Ling Ye Xu Cao Xuedong Zhou 《Bone Research》 CAS CSCD 2018年第4期383-392,共10页
There is currently no effective medical treatment for temporomandibular joint osteoarthritis(TMJ-OA) due to a limited understanding of its pathogenesis. This study was undertaken to investigate the key role of transfo... There is currently no effective medical treatment for temporomandibular joint osteoarthritis(TMJ-OA) due to a limited understanding of its pathogenesis. This study was undertaken to investigate the key role of transforming growth factor-β(TGF-β)signalling in the cartilage and subchondral bone of the TMJ using a temporomandibular joint disorder(TMD) rat model, an ageing mouse model and a Camurati–Engelmann disease(CED) mouse model. In the three animal models, the subchondral bone phenotypes in the mandibular condyles were evaluated by μCT, and changes in TMJ condyles were examined by TRAP staining and immunohistochemical analysis of Osterix and p-Smad2/3. Condyle degradation was confirmed by Safranin O staining, the Mankin and OARSI scoring systems and type X collagen(Col X), p-Smad2/3 a and Osterix immunohistochemical analyses. We found apparent histological phenotypes of TMJ-OA in the TMD, ageing and CED animal models, with abnormal activation of TGF-βsignalling in the condylar cartilage and subchondral bone. Moreover, inhibition of TGF-β receptor I attenuated TMJ-OA progression in the TMD models. Therefore, aberrant activation of TGF-β signalling could be a key player in TMJ-OA development. 展开更多
关键词 TMJ OA TMD Aberrant activation of latent transforming growth factor initiates the onset of temporomandibular joint osteoarthritis
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A State-Migration Particle Swarm Optimizer for Adaptive Latent Factor Analysis of High-Dimensional and Incomplete Data
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作者 Jiufang Chen Kechen Liu +4 位作者 Xin Luo Ye Yuan Khaled Sedraoui Yusuf Al-Turki MengChu Zhou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI 2024年第11期2220-2235,共16页
High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation lear... High-dimensional and incomplete(HDI) matrices are primarily generated in all kinds of big-data-related practical applications. A latent factor analysis(LFA) model is capable of conducting efficient representation learning to an HDI matrix,whose hyper-parameter adaptation can be implemented through a particle swarm optimizer(PSO) to meet scalable requirements.However, conventional PSO is limited by its premature issues,which leads to the accuracy loss of a resultant LFA model. To address this thorny issue, this study merges the information of each particle's state migration into its evolution process following the principle of a generalized momentum method for improving its search ability, thereby building a state-migration particle swarm optimizer(SPSO), whose theoretical convergence is rigorously proved in this study. It is then incorporated into an LFA model for implementing efficient hyper-parameter adaptation without accuracy loss. Experiments on six HDI matrices indicate that an SPSO-incorporated LFA model outperforms state-of-the-art LFA models in terms of prediction accuracy for missing data of an HDI matrix with competitive computational efficiency.Hence, SPSO's use ensures efficient and reliable hyper-parameter adaptation in an LFA model, thus ensuring practicality and accurate representation learning for HDI matrices. 展开更多
关键词 Data science generalized momentum high-dimensional and incomplete(HDI)data hyper-parameter adaptation latent factor analysis(LFA) particle swarm optimization(PSO)
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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning 被引量:5
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作者 Xin Luo Wen Qin +2 位作者 Ani Dong Khaled Sedraoui MengChu Zhou 《IEEE/CAA Journal of Automatica Sinica》 SCIE EI CSCD 2021年第2期402-411,共10页
A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning algorithm.However,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and... A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning algorithm.However,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial problems.Aiming at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training process.With it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality recommendations.Experimental results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability. 展开更多
关键词 Big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system(RS) stochastic gradient descent(SGD)
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Realized volatility forecast of financial futures using timevarying HAR latent factor models 被引量:1
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作者 Jiawen Luo Zhenbiao Chen Shengquan Wang 《Journal of Management Science and Engineering》 CSCD 2023年第2期214-243,共30页
We forecast realized volatilities by developing a time-varying heterogeneous autoregressive(HAR)latent factor model with dynamic model average(DMA)and dynamic model selection(DMS)approaches.The number of latent factor... We forecast realized volatilities by developing a time-varying heterogeneous autoregressive(HAR)latent factor model with dynamic model average(DMA)and dynamic model selection(DMS)approaches.The number of latent factors is determined using Chan and Grant's(2016)deviation information criteria.The predictors in our model include lagged daily,weekly,and monthly volatility variables,the corresponding volatility factors,and a speculation variable.In addition,the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models,including size,inclusion probabilities,and coefficients,are examined.We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts.Furthermore,the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China. 展开更多
关键词 Realized volatility forecast HAR latent factor models Bayesian approaches TIME-VARYING Stock index Treasury bond futures
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Two of a kind or the ratings game? Adaptive pairwise preferences and latent factor models 被引量:1
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作者 SuhridBALAKRISHNAN SumitCHOPRA 《Frontiers of Computer Science》 SCIE EI CSCD 2012年第2期197-208,共12页
Latent factor models have become a workhorse for a large number of recommender systems. While these sys- tems are built using ratings data, which is typically assumed static, the ability to incorporate different kinds... Latent factor models have become a workhorse for a large number of recommender systems. While these sys- tems are built using ratings data, which is typically assumed static, the ability to incorporate different kinds of subsequent user feedback is an important asset. For instance, the user might want to provide additional information to the system in order to improve his personal recommendations. To this end, we examine a novel scheme for efficiently learning (or refining) user parameters from such feedback. We propose a scheme where users are presented with a sequence of pair- wise preference questions: "Do you prefer item A over B?" User parameters are updated based on their response, and subsequent questions are chosen adaptively after incorporat- ing the feedback. We operate in a Bayesian framework and the choice of questions is based on an information gain cri- terion. We validate the scheme on the Netflix movie ratings data set and a proprietary television viewership data set. A user study and automated experiments validate our findings. 展开更多
关键词 recommender systems latent factor models pairwise preferences active learning
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Improved Hybrid Collaborative Fitering Algorithm Based on Spark Platform 被引量:1
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作者 YOU Zhen HU Hongwen +2 位作者 WANG Yutao XUE Jinyun YI Xinwu 《Wuhan University Journal of Natural Sciences》 CAS CSCD 2023年第5期451-460,共10页
An improved Hybrid Collaborative Filtering algorithm(H-CF)is proposed,addressing the issues of data sparsity,low recommendation accuracy,and poor scalability present in traditional collaborative filtering algorithms.T... An improved Hybrid Collaborative Filtering algorithm(H-CF)is proposed,addressing the issues of data sparsity,low recommendation accuracy,and poor scalability present in traditional collaborative filtering algorithms.The core of H-CF is a linear weighted hybrid algorithm based on the Latent Factor Model(LFM)and the Improved Item Clustering and Similarity Calculation Collaborative Filtering Algorithm(ITCSCF).To begin with,the items are clustered based on their attribute dimension,which accelerates the computation of the nearest neighbor set.Subsequently,H-CF enhances the formula for scoring similarity by penalizing popular items and optimizing unpopular items.This improvement enhances the rationality of scoring similarity and reduces the impact of data sparseness.Furthermore,a weighting function is employed to combine the various improved algorithms.The balance factor of the weighting function is dynamically adjusted to attain the optimal recommendation list.To address the real-time and scalability concerns,the algorithm leverages the Spark big data distributed cluster computing framework.Experiments were conducted using the public dataset Movie Lens,where the improved algorithm’s performance was compared against the algorithm before enhancement and the algorithm running on a single machine.The experimental results demonstrate that the improved algorithm outperforms in terms of data sparsity,recommendation personalization,accuracy,recall,and efficiency. 展开更多
关键词 recommendation algorithm collaborative filtering latent factor model score weighting item clustering SPARK similarity calculation
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Recommendation Algorithm Based on Improved Convolutional Neural Network and Matrix Factorization
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作者 Shengbin Liang Lulu Bai Hengming Zhang 《国际计算机前沿大会会议论文集》 2020年第1期642-654,共13页
The traditional collaborative filtering algorithm uses the user rating information as a recommendation basis,but the ratings matrices are usually sparse and cannot reflect users’preference exactly,so the recommendati... The traditional collaborative filtering algorithm uses the user rating information as a recommendation basis,but the ratings matrices are usually sparse and cannot reflect users’preference exactly,so the recommendation results are not very accurate.Therefore,this paper proposes an improved convolutional neural network for collaborative filtering(CNNCF),using the deep learning model to deeply mine the hidden feature information.then implicit the semantic model,Then the extracted explicit feature information was replaced by the implicit feature information in the LFM to further improve the prediction accuracy,and finally personalized recommendation through the user-item preference matrix.Experimental results on the MovieLens dataset show that the model can overcome data sparse,and recommendation accuracy is better than the traditional collaborative filtering model. 展开更多
关键词 Collaborative filtering latent factor model Convolutional neural network Recommender System Hybrid recommendation
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