Results 1 to 10 of about 130 (119)
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
Stefanos Zafeiriou
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Sparse Nonnegative Tensor Factorization and Completion With Noisy Observations [PDF]
IEEE Transactions on Information Theory, 2022 In this paper, we study the sparse nonnegative tensor factorization and completion problem from partial and noisy observations for third-order tensors. Because of sparsity and nonnegativity, the underlying tensor is decomposed into the tensor-tensor product of one sparse nonnegative tensor and one nonnegative tensor.
Michael Ng, Xiongjun Zhang
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On Tensors, Sparsity, and Nonnegative Factorizations [PDF]
SIAM Journal on Matrix Analysis and Applications, 2012 Tensors have found application in a variety of fields, ranging from chemometrics to signal processing and beyond. In this paper, we consider the problem of multilinear modeling of sparse count data. Our goal is to develop a descriptive tensor factorization model of such data, along with appropriate algorithms and theory.
Tamara G. Kolda, Eric C Chi
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Nonnegative Tensor Factorization, Completely Positive Tensors, and a Hierarchical Elimination Algorithm [PDF]
SIAM Journal on Matrix Analysis and Applications, 2014 Nonnegative tensor factorization has applications in statistics, computer vision, exploratory multiway data analysis, and blind source separation. A symmetric nonnegative tensor, which has an exact symmetric nonnegative factorization, is called a completely positive tensor. This concept extends the concept of completely positive matrices.
Liqun Qi, Changqing Xu
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HyperNTF: A Hypergraph Regularized Nonnegative Tensor Factorization for Dimensionality Reduction
Neurocomputing, 2022 15 ...
Zhengming Ma, Quanying Liu, Wanguang Yin
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Coseparable Nonnegative Tensor Factorization With t-CUR Decomposition
SIAM Journal on Matrix Analysis and ApplicationsAbstract. Nonnegative matrix factorization (NMF) is an important unsupervised learning method to extract meaningful features from data. To address the NMF problem within a polynomial time framework, researchers have introduced a separability assumption, which has recently evolved into the concept of coseparability.
Yimin Wei, Longxiu Huang
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Image Clustering Algorithm Based on Hypergraph Regularized Nonnegative Tucker Decomposition [PDF]
Jisuanji gongcheng, 2022 The internal geometry structure of high-dimensional data is ignored when nonnegative tensor decomposition is applied to image clustering.To solve this problem, we propose a Hypergraph regularized Nonnegative Tucker Decomposition(HGNTD) model by adding a ...
CHEN Luyao, LIU Qilong, XU Yunxia, CHEN Zhen
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Alzheimer’s Disease Recognition Applying Non-Negative Matrix Factorization Characteristics from Brain Magnetic Resonance Images (MRI) [PDF]
E3S Web of Conferences, 2023 To more accurately depict Alzheimer’s disease (AD) and projecting clinical outcomes while taking into account advancements in clinical imaging and substantial learning, several experts are gradually using ConvNet (CNNs) to remove deep intensity features ...
Reddy G. Vijendar +4 more
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Sparse and Low-Rank Constrained Tensor Factorization for Hyperspectral Image Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Third-order tensors have been widely used in hyperspectral remote sensing because of their ability to maintain the 3-D structure of hyperspectral images.
Pan Zheng, Hongjun Su, Qian Du
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IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Hyperspectral unmixing aims to separate pure materials and their corresponding proportions that constitute the mixed pixels of hyperspectral imagery (HSI). Recently, the matrix-vector nonnegative tensor factorization (MV-NTF) has attracted wide attention
Ping Yang +3 more
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