Results 11 to 20 of about 13,782,934 (217)

Stratified Non-Negative Tensor Factorization

2024 58th Asilomar Conference on Signals, Systems, and Computers
5 pages.
Deanna Needell
exaly   +6 more sources

Spatial Low-Rank Tensor Factorization and Unmixing of Hyperspectral Images

Computers, 2021
This work presents a method for hyperspectral image unmixing based on non-negative tensor factorization. While traditional approaches may process spectral information without regard for spatial structures in the dataset, tensor factorization preserves ...
William Navas-Auger, Vidya Manian
doaj   +2 more sources

A non-negative tensor factorization model for selectional preference induction [PDF]

Natural Language Engineering, 2009
AbstractThe distributional similarity methods have proven to be a valuable tool for the induction of semantic similarity. Until now, most algorithms use two-way co-occurrence data to compute the meaning of words. Co-occurrence frequencies, however, need not be pairwise.
Van de Cruys, T., Tim Van de Cruys, Van de Cruys, Tim   +2 more
openaire   +5 more sources

Non-Negative Tensor Factorization Applied to Music Genre Classification [PDF]

IEEE Transactions on Audio, Speech, and Language Processing, 2010
Music genre classification techniques are typically applied to the data matrix whose columns are the feature vectors extracted from music recordings. In this paper, a feature vector is extracted using a texture window of one sec, which enables the representation of any 30 sec long music recording as a time sequence of feature vectors, thus yielding a ...
Emmanouil Benetos, Constantine Kotropoulos   +1 more
openaire   +6 more sources

Non-negative tensor factorization with applications to statistics and computer vision [PDF]

Proceedings of the 22nd international conference on Machine learning - ICML '05, 2005
We derive algorithms for finding a non-negative n-dimensional tensor factorization (n-NTF) which includes the non-negative matrix factorization (NMF) as a particular case when n = 2. We motivate the use of n-NTF in three areas of data analysis: (i) connection to latent class models in statistics, (ii) sparse image coding in computer vision, and (iii ...
Amnon Shashua, Tamir Hazan
openaire   +3 more sources

Generalized Discriminant Orthogonal Nonnegative Tensor Factorization for Facial Expression Recognition [PDF]

The Scientific World Journal, 2014
In order to overcome the limitation of traditional nonnegative factorization algorithms, the paper presents a generalized discriminant orthogonal non-negative tensor factorization algorithm.
Zhang XiuJun, Liu Chang
doaj   +2 more sources

Non-negative Tensor Factorization for single-channel EEG artifact rejection [PDF]

2013 IEEE International Workshop on Machine Learning for Signal Processing (MLSP), 2013
New applications of Electroencephalographic recording (EEG) pose new challenges in terms of artifact removal. In our work, we target informed source separation methods for artifact removal in single-channel EEG recordings by exploiting prior knowledge from auxiliary lightweight sensors capturing artifactual signals.
Damon, Cécilia, Liutkus, Antoine, Gramfort, Alexandre, Essid, Slim   +3 more
openaire   +3 more sources

Multi-View Clustering via Semi-non-negative Tensor Factorization [PDF]

CoRR, 2023
Multi-view clustering (MVC) based on non-negative matrix factorization (NMF) and its variants have received a huge amount of attention in recent years due to their advantages in clustering interpretability. However, existing NMF-based multi-view clustering methods perform NMF on each view data respectively and ignore the impact of between-view.
Jing Li 0026, Quanxue Gao, Qianqian Wang 0001, Wei Xia 0007, Xinbo Gao 0001   +4 more
openaire   +3 more sources

Non-negative Einstein tensor factorization for unmixing hyperspectral images [PDF]

Numerical Algorithms
In this manuscript, we introduce a tensor-based approach to Non-Negative Tensor Factorization (NTF). The method entails tensor dimension reduction through the utilization of the Einstein product. To maintain the regularity and sparsity of the data, certain constraints are imposed.
Anas El Hachimi, Khalide Jbilou, Ahmed Ratnani   +2 more
core   +5 more sources

Detecting the community structure and activity patterns of temporal networks: a non-negative tensor factorization approach.

PLoS ONE, 2014
The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding
Laetitia Gauvin, André Panisson, Ciro Cattuto   +2 more
doaj   +2 more sources