Results 21 to 30 of about 9,083 (186)
Renormalization group flows of Hamiltonians using tensor networks [PDF]
, 2017 A renormalization group flow of Hamiltonians for two-dimensional classical partition functions is constructed using tensor networks. Similar to tensor network renormalization ([G. Evenbly and G. Vidal, Phys. Rev. Lett. 115, 180405 (2015)], [S. Yang, Z.-C.
Bal, Matthias +3 more
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Classification Analysis of Tensor-Based Recovered Missing EEG Data
IEEE Access, 2021 This paper discusses impact of recovering missing Electroencephalography (EEG) data on classification accuracy of hand movements using tensor-based methods.
Muhammad Akmal +2 more
doaj +1 more source
Journal of Big Data, 2021 Detecting and delineating hot spots in data from radiation sensors is required in applications ranging from monitoring large geospatial areas to imaging small objects in close proximity.
Michael G. Thomason, Benjamin S. Jordan
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Modeling Hierarchical Seasonality Through Low-Rank Tensor Decompositions in Time Series Analysis
IEEE Access, 2023 Accurately representing periodic behavior is a frequently encountered challenge in modeling time series. This is especially true for observations where multiple, nested seasonalities are present, which is often encountered in data that pertain to ...
Melih Barsbey, Ali Taylan Cemgil
doaj +1 more source
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.
Chi, Eric C., Kolda, Tamara G.
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Discriminative Nonnegative Tucker Decomposition for Tensor Data Representation
Mathematics, 2022 Nonnegative Tucker decomposition (NTD) is an unsupervised method and has been extended in many applied fields. However, NTD does not make use of the label information of sample data, even though such label information is available.
Wenjing Jing, Linzhang Lu, Qilong Liu
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Nonnegative approximations of nonnegative tensors [PDF]
, 2009 We study the decomposition of a nonnegative tensor into a minimal sum of outer product of nonnegative vectors and the associated parsimonious naive Bayes probabilistic model.
Comon, Pierre, Lim, Lek-Heng
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iScience, 2022 Summary: Symptoms of adverse reactions to vaccines evolve over time, but traditional studies have focused only on the frequency and intensity of symptoms.
Kei Ikeda +16 more
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Using Underapproximations for Sparse Nonnegative Matrix Factorization [PDF]
, 2008 Nonnegative Matrix Factorization consists in (approximately) factorizing a nonnegative data matrix by the product of two low-rank nonnegative matrices. It has been successfully applied as a data analysis technique in numerous domains, e.g., text mining ...
Anstreicher +24 more
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Blind multispectral image decomposition by 3D nonnegative tensor factorization [PDF]
Optics Letters, 2009 Alpha-divergence-based nonnegative tensor factorization (NTF) is applied to blind multispectral image (MSI) decomposition. The matrix of spectral profiles and the matrix of spatial distributions of the materials resident in the image are identified from the factors in Tucker3 and PARAFAC models.
Kopriva, Ivica, Cichocki, Andrzej
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