Results 171 to 180 of about 223,331 (198)

Tensor-factorized neural networks

IEEE Transactions on Neural Networks and Learning Systems, 2018
The growing interests in multiway data analysis and deep learning have drawn tensor factorization (TF) and neural network (NN) as the crucial topics. Conventionally, the NN model is estimated from a set of one-way observations. Such a vectorized NN is not generalized for learning the representation from multiway observations.
Jen-Tzung Chien, Yi-Ting Bao
openaire   +3 more sources

Probabilistic Latent Tensor Factorization

2010
We develop a probabilistic framework for multiway analysis of high dimensional datasets. By exploiting a link between graphical models and tensor factorization models we can realize any arbitrary tensor factorization structure, and many popular models such as CP or TUCKER models with Euclidean error and their non-negative variants with KL error appear ...
Yusuf Kenan Yilmaz, A. Taylan Cemgil
openaire   +2 more sources

Tensor factorization for relational learning

2013
Relational learning is concerned with learning from data where information is primarily represented in form of relations between entities. In recent years, this branch of machine learning has become increasingly important, as relational data is generated in an unprecedented amount and has become ubiquitous in many fields of application such as ...
openaire   +2 more sources

Constrained Nonnegative Tensor Factorization for Clustering

2010 Ninth International Conference on Machine Learning and Applications, 2010
Constrained clustering through matrix factorization has been shown to largely improve clustering accuracy by incorporating prior knowledge into the factorization process. Although it has been well studied, none of them deal with constrained multi-way data factorization. Multi-way data or Tensors are encoded as high-order data structures.
openaire   +4 more sources

A Weighted Tensor Factorization Method for Low-Rank Tensor Completion

2019 IEEE Fifth International Conference on Multimedia Big Data (BigMM), 2019
Recently, low-rank tensor completion has attracted increasing attention in recovering incomplete tensor whose elements are missing. The basic assumption is that the underlying tensor is a low-rank tensor, and therefore tensor nuclear norm minimization can be applied to recover such tensor.
Miaomiao Cheng, Liping Jing, Michael Kwok-Po Ng   +2 more
openaire   +2 more sources

Tensor factorization-based particle swarm optimization for large-scale many-objective problems

Swarm and Evolutionary Computation, 2022
Shuang Wei
exaly  

Inconsistent tensor-tensor product for low-rank tensor factorization

Applied Mathematics Letters
Sheng Liu 0033, Xi-Le Zhao, Qin Jiang
openaire   +1 more source

Logarithmic Norm Regularized Low-Rank Factorization for Matrix and Tensor Completion

IEEE Transactions on Image Processing, 2021
Lin Chen, Xue Jiang, Xingzhao Liu
exaly  

Bayesian CP Factorization of Incomplete Tensors with Automatic Rank Determination

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015
Liqing Zhang, Qibin ZHAO, Andrzej Cichocki   +2 more
exaly  

TrustTF: A tensor factorization model using user trust and implicit feedback for context-aware recommender systems

Knowledge-Based Systems, 2020
Jianli Zhao, Huan Huo, Lijun Qu
exaly