Results 21 to 30 of about 150,932 (264)
Beyond Low Rank + Sparse: Multiscale Low Rank Matrix Decomposition [PDF]
IEEE Journal on Selected Topics in Signal Processing, 2016 Michael Lustig, Frank Ong
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Jordan Matrix Decomposition [PDF]
Formalized Mathematics, 2008 We follow the rules: i, j, m, n, k denote natural numbers, K denotes a field, and a, λ denote elements of K. Let us consider K, λ, n. The Jordan block of λ and n yields a matrix over K and is defined by the conditions (Def. 1). (Def. 1)(i) len (the Jordan block of λ and n) = n, (ii) width (the Jordan block of λ and n) = n, and (iii) for all i, j such ...
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Fast Superpixel Based Subspace Low Rank Learning Method for Hyperspectral Denoising
IEEE Access, 2018 Sequential data, such as video frames and event data, have been widely applied in the realworld. As a special kind of sequential data, hyperspectral images (HSIs) can be regarded as a sequence of 2-D images in the spectral dimension, which can be ...
Le Sun +5 more
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Weighted Sparseness-Based Anomaly Detection for Hyperspectral Imagery
Sensors, 2023 Anomaly detection of hyperspectral remote sensing data has recently become more attractive in hyperspectral image processing. The low-rank and sparse matrix decomposition-based anomaly detection algorithm (LRaSMD) exhibits poor detection performance in ...
Xing Lian +6 more
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IEEE Access, 2019 Opportunistic network enables users to form an instant network for data sharing, which is a type of Ad-hoc network in nature, thus depends on cooperation between nodes to complete message transmission.
Peng Zheng +3 more
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Multi-model deep learning approach for collaborative filtering recommendation system
CAAI Transactions on Intelligence Technology, 2020 As a result of a huge volume of implicit feedback such as browsing and clicks, many researchers are involving in designing recommender systems (RSs) based on implicit feedback.
Mohammed Fadhel Aljunid +1 more
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Column-coherent matrix decomposition
Data Mining and Knowledge Discovery, 2023 AbstractMatrix decomposition is a widely used tool in machine learning with many applications such as dimension reduction or visualization. In this paper we consider decomposing X, a matrix of size $$n \times m$$ n × m , to a product WS ...
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Molecules, 2020 Conventional proton nuclear magnetic resonance (1H-NMR) has been widely used for identification and quantification of small molecular components in food.
Feifei Wei +6 more
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Mueller matrix differential decomposition [PDF]
Optics Letters, 2011 We present a Mueller matrix decomposition based on the differential formulation of the Mueller calculus. The differential Mueller matrix is obtained from the macroscopic matrix through an eigenanalysis. It is subsequently resolved into the complete set of 16 differential matrices that correspond to the basic types of optical behavior for depolarizing ...
Ortega-Quijano, Noé +1 more
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Computation with No Memory, and Rearrangeable Multicast Networks [PDF]
Discrete Mathematics & Theoretical Computer Science, 2014 We investigate the computation of mappings from a set S^n to itself with "in situ programs", that is using no extra variables than the input, and performing modifications of one component at a time, hence using no extra memory.
Emeric Gioan +2 more
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