Results 81 to 90 of about 223,331 (198)
Supervised nonnegative tensor factorization with Maximum-Margin Constraint
, 2013 Non-negative tensor factorization (NTF) has attracted great attention in the machine learning community. In this paper, we extend traditional non-negative tensor factorization into a supervised discriminative decomposition, referred as Supervised Non ...
Zhuang, Yueting +17 more
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Tagrec-CMTF: Coupled Matrix and Tensor Factorization for Tag Recommendation
IEEE Access, 2018 In order to address data sparsity, missing value, and over-fitting problems in a social tagging system, a coupled matrix and tensor factorization (CMTF) method named Tagrec-CMTF for tag recommendation is proposed in this paper.
Yi Yang +5 more
doaj +1 more source
Discriminant Nonnegative Tensor Factorization Algorithms [PDF]
IEEE Transactions on Neural Networks, 2009 Nonnegative matrix factorization (NMF) has proven to be very successful for image analysis, especially for object representation and recognition. NMF requires the object tensor (with valence more than one) to be vectorized. This procedure may result in information loss since the local object structure is lost due to vectorization. Recently, in order to
openaire +2 more sources
A Survey of Tensor Factorization Frameworks on Audio Modelling
, 2014 : This survey is about Tensor Factorization methods for audio modeling, which focuses on probabilistic latent tensor factorization and generalized coupled tensor factorization by expectation maximization method while using several linear and nonlinear ...
Ünsal Gökdağ
core
User social influence analysis based on constrained nonnegative tensor factorization
Tongxin xuebao, 2016 Existing models for measuring user social influence fail to integrate both opinion and topic information.Therefore,a new constrained nonnegative tensor factorization method combining user’s opinion and the topical relevance was proposed.The method ...
Jing-jing WEI +4 more
doaj +2 more sources
Tensor factorization for multi-relational learning
, 2013 . Tensor factorization has emerged as a promising approach for solving relational learning tasks. Here we review recent results on a particular tensor factorization approach, i.e.
Volker Tresp, Maximilian Nickel
core
Distributed Methods for High-dimensional and Large-scale Tensor Factorization
, 2014 —Given a high-dimensional and large-scale tensor, how can we decompose it into latent factors? Can we process it on commodity computers with limited memory?
U. Kang, Kijung Shin, U Kang
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Robust Bayesian Tensor Factorization with Zero-Inflated Poisson Model and Consensus Aggregation [PDF]
, 2023 Tensor factorizations (TF) are powerful tools for the efficient representation and analysis of multidimensional data. However, classic TF methods based on maximum likelihood estimation underperform when applied to zero-inflated count data, such as single-
Chafamo, Daniel +2 more
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C.: Controlling sparseness in nonnegative tensor factorization
, 2006 . Non-negative tensor factorization (NTF) has recently been proposed as sparse and efficient image representation (Welling and Weber, Patt. Rec. Let., 2001). Until now, sparsity of the tensor factorization has been empirically observed in many cases, but
Christoph Schnörr, Matthias Heiler
core
Abnormal behavior detection using tensor factorization [PDF]
, 2016 Real Time Location Systems (RTLS) using RFID is a popular surveillance method for security. However, in an open and dynamic environment, where patterns rarely repeat, it is difficult to implement a model that could analyse all the information generated ...
Shah, Alpa Jayesh
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