Results 21 to 30 of about 145,037 (166)
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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Relative-Error $CUR$ Matrix Decompositions [PDF]
SIAM Journal on Matrix Analysis and Applications, 2008 Many data analysis applications deal with large matrices and involve approximating the matrix using a small number of ``components.'' Typically, these components are linear combinations of the rows and columns of the matrix, and are thus difficult to interpret in terms of the original features of the input data.
Drineas, Petros +2 more
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Singular random matrix decompositions: Jacobians [PDF]
Journal of Multivariate Analysis, 2005 For a singular random matrix Y, we find the Jacobians associated with the following decompositions; QR, Polar, Singular Value (SVD), L'U, L'DM and modified QR (QDR). Similarly, we find the Jacobinas of the following decompositions: Spectral, Cholesky's, L'DL and symmetric non-negative definite square root, of the cross-product matrix S = Y'Y.
González Farías, Graciela +1 more
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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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Singular random matrix decompositions: distributions [PDF]
Journal of Multivariate Analysis, 2005 Assuming that Y has a singular matrix variate elliptically contoured distribution with respect to the Hausdorff measure, the distributions of several matrices associated to QR, modified QR, SV and Polar decompositions of matrix Y are determined, for central and non-central, non-singular and singular cases, as well as their relationship to the Wishart ...
González Farías, Graciela +1 more
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Randomized Matrix Decompositions Using R [PDF]
Journal of Statistical Software, 2019 Matrix decompositions are fundamental tools in the area of applied mathematics, statistical computing, and machine learning. In particular, low-rank matrix decompositions are vital, and widely used for data analysis, dimensionality reduction, and data compression.
Erichson, N. Benjamin +3 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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