In the multimedia big data, the demand for personalized multimedia recommendation algorithm is increasing to ease the multimedia information overload. The multimedia recommendation system has been applied in various i...
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In the multimedia big data, the demand for personalized multimedia recommendation algorithm is increasing to ease the multimedia information overload. The multimedia recommendation system has been applied in various industries and has been playing a significant role. With the development of multimedia big data, developing multimedia recommendation algorithms can effectively be used in multimedia data. However, a large number of prevailing recommendation systems cannot meet the multimedia recommendation requirements, since they ignore the user-item interactions with multimedia content. This essay realizes the multimedia recommendation based on probability graphical model, to deal with the cold start and data sparsity involved in collaborative filtering recommendation, proposing that add the user tag to user-item model. The essay optimizes the multimedia recommendation algorithm based on undirected graphicalmodel and tests it with singular value decomposition, clustering and Naive Bayes separately. The essay also builds the checklist recommendation model and experiments extensively for comparison with the conditional multimedia recommendation algorithm, by using PersonalRank algorithm based on random-walk to work out the weight coefficient of the user tag. At the same time, the essay enhances the probability-graph multimedia recommendation algorithm by dimensionality reduction and clustering, with the result of noticeably improved precision and recall.
Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the t...
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Sparse nonnegative matrix factorization (SNMF) aims to factorize a data matrix into two optimized nonnegative sparse factor matrices, which could benefit many tasks, such as document-word co-clustering. However, the traditional SNMF typically assumes the number of latent factors (i.e., dimensionality of the factor matrices) to be fixed. This assumption makes it inflexible in practice. In this paper, we propose a doubly sparse nonparametric NMF framework to mitigate this issue by using dependent Indian buffet processes (dIBP). We apply a correlation function for the generation of two stick weights associated with each column pair of factor matrices while still maintaining their respective marginal distribution specified by IBP. As a consequence, the generation of two factor matrices will be columnwise correlated. Under this framework, two classes of correlation function are proposed: 1) using bivariate Beta distribution and 2) using Copula function. Compared with the single IBP-based NMF, this paper jointly makes two factor matrices nonparametric and sparse, which could be applied to broader scenarios, such as co-clustering. This paper is seen to be much more flexible than Gaussian process-based and hierarchial Beta process-based dIBPs in terms of allowing the two corresponding binary matrix columns to have greater variations in their nonzero entries. Our experiments on synthetic data show the merits of this paper compared with the state-of-the-art models in respect of factorization efficiency, sparsity, and flexibility. Experiments on real-world data sets demonstrate the efficiency of this paper in document-word co-clustering tasks.
Complex chemical processes usually operate at multiple operating modes, resulting from various factors, such as changes in market demand, set point modifications, and feedstock changes. It is difficult to monitor a mu...
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Complex chemical processes usually operate at multiple operating modes, resulting from various factors, such as changes in market demand, set point modifications, and feedstock changes. It is difficult to monitor a multimode process without generating significant number of false alarms. In this paper, a risk-based alarm system design methodology is proposed to monitor multimode processes. The methodology comprises of three main steps: i) analysis of operating data using Gaussian mixture model, identification of independent operating modes (e.g. set points, virtual transient state), and iii) probabilistic model to assess risk and activation of appropriate warning. A continuous stirred tank reactor with model predictive control system is used to demonstrate the effectiveness of the proposed method. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. Ail rights reserved.
An important task in big data integration is to derive accurate data records from noisy and conflicting values collected from multiple sources. Most existing truth finding methods assume that the reliability is consis...
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An important task in big data integration is to derive accurate data records from noisy and conflicting values collected from multiple sources. Most existing truth finding methods assume that the reliability is consistent on the whole data set, ignoring the fact that different attributes, objects and object groups may have different reliabilities even wrt the same source. These reliability differences are caused by the hardness differences in obtaining attribute values, non-uniform updates to objects and the differences in group privileges. This paper addresses the problem how to compute truths by effectively estimating the reliabilities of attributes, objects and object groups in a multi-source heterogeneous data environment. We first propose an optimization framework TFAR, its implementation and Lagrangian duality solution for Truth Finding by Attribute Reliability estimation. We then present a Bayesian probabilistic graphicalmodel TFOR and an inference algorithm applying Collapsed Gibbs Sampling for Truth Finding by Object Reliability estimation. Finally we give an optimization framework TFGR and its implementation for Truth Finding by Group Reliability estimation. All these models lead to a more accurate estimation of the respective attribute, object and object group reliabilities, which in turn can achieve a better accuracy in inferring the truths. Experimental results on both real data and synthetic data show that our methods have better performance than the state-of-art truth discovery methods. (C) 2019 Elsevier B.V. All rights reserved.
In this work, a high efficiency coding unit (CU) size decision algorithm is proposed for high efficiency video coding (HEVC) inter coding. The CU splitting or non-splitting is modeled as a binary classification proble...
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In this work, a high efficiency coding unit (CU) size decision algorithm is proposed for high efficiency video coding (HEVC) inter coding. The CU splitting or non-splitting is modeled as a binary classification problem based on probability graphical model (PGM). This method incorporates two sub-methods: CU size termination decision and CU size skip decision. This method focuses on the trade-off between encoding efficiency and encoding complexity, and it has a good performance. Particularly in the high resolution application, simulation results demonstrate that the proposed algorithm can reduce encoding time by 53.62%-57.54%, while the increased BD-rate are only 1.27%-1.65%, compared to the HEVC software model.
Complex chemical processes usually operate at multiple operating modes, resulting from various factors, such as changes in market demand, set point modifications, and feedstock changes. It is difficult to monitor a mu...
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Complex chemical processes usually operate at multiple operating modes, resulting from various factors, such as changes in market demand, set point modifications, and feedstock changes. It is difficult to monitor a multimode process without generating significant number of false alarms. In this paper, a risk-based alarm system design methodology is proposed to monitor multimode processes. The methodology comprises of three main steps: i) analysis of operating data using Gaussian mixture model, ii) identification of independent operating modes (e.g. set points, virtual transient state), and iii) probabilistic model to assess risk and activation of appropriate warning. A continuous stirred tank reactor with model predictive control system is used to demonstrate the effectiveness of the proposed method.
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