Results 21 to 30 of about 38,023 (256)
Sparse Constrained Low Tensor Rank Representation Framework for Hyperspectral Unmixing
Remote Sensing, 2021 Recently, non-negative tensor factorization (NTF) as a very powerful tool has attracted the attention of researchers. It is used in the unmixing of hyperspectral images (HSI) due to its excellent expression ability without any information loss when ...
Le Dong, Yuan Yuan
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Weighted Group Sparsity-Constrained Tensor Factorization for Hyperspectral Unmixing
Remote Sensing, 2022 Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels.
Xinxi Feng, Le Han, Le Dong
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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 +3 more
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Sample Complexity of Dictionary Learning and other Matrix Factorizations [PDF]
, 2015 Many modern tools in machine learning and signal processing, such as sparse dictionary learning, principal component analysis (PCA), non-negative matrix factorization (NMF), $K$-means clustering, etc., rely on the factorization of a matrix obtained by ...
Bach, Francis +4 more
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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
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A tensor-based approach for automatic music genre classification [PDF]
, 2008 Most music genre classification techniques employ pattern recognition algorithms to classify feature vectors extracted from recordings into genres.
Benetos, E., Kotropoulos, C.
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Using Matrix and Tensor Factorizations for the Single-Trial Analysis of Population Spike Trains. [PDF]
PLoS Computational Biology, 2016 Advances in neuronal recording techniques are leading to ever larger numbers of simultaneously monitored neurons. This poses the important analytical challenge of how to capture compactly all sensory information that neural population codes carry in ...
Arno Onken +5 more
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Non-negative tensor factorization models for Bayesian audio processing
Digital Signal Processing, 2015 We provide an overview of matrix and tensor factorization methods from a Bayesian perspective, giving emphasis on both the inference methods and modeling techniques. Factorization based models and their many extensions such as tensor factorizations have proved useful in a broad range of applications, supporting a practical and computationally tractable
Simsekli, Umut +2 more
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IEEE Access, 2020 The fusion of hyperspectral and multispectral images is an effective way to obtain hyperspectral super-resolution images with high spatial resolution. A hyperspectral image is a datacube containing two spatial dimensions and a spectral dimension.
Fei Ma, Feixia Yang, Yanwei Wang
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Multi-View Clustering via Semi-non-negative Tensor Factorization
, 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.
Li, Jing +4 more
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