Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers

DOI10.1002/sam.11407OpenAlexW2916485956WikidataQ118593856 ScholiaQ118593856MaRDI QIDQ4970264

Valentin G. Stanev, K.Ø. Rasmussen, Boian S. Alexandrov, Velimir V. Vesselinov

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.11407

zbMATH Keywords

feature extractiondimension reductionphase separationunsupervised learningnonnegative tensor factorization

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Uses Software

• Silhouettes
• N-way Toolbox
• PARAFAC

Cites Work

• Tensor Decompositions and Applications
• PARAFAC: Parallel factor analysis
• On uniqueness in CANDECOMP/PARAFAC
• Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
• Identification of release sources in advection-diffusion system by machine learning combined with Green's function inverse method
• Alternating proximal gradient method for sparse nonnegative Tucker decomposition
• Singular value decomposition and least squares solutions
• A Block Coordinate Descent Method for Regularized Multiconvex Optimization with Applications to Nonnegative Tensor Factorization and Completion
• Uniqueness of Nonnegative Tensor Approximations
• Tensor rank is NP-complete
• A Multilinear Singular Value Decomposition
• Tensor Decomposition for Signal Processing and Machine Learning
• The Equilibrium Theory of Inhomogeneous Polymers
• Learning the parts of objects by non-negative matrix factorization

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