Tensor Decomposition: A Mathematical Tool for Data Analysis.

Title: Tensor Decomposition: A Mathematical Tool for Data Analysis.

Full Record
Other Related Research

Abstract

Abstract not provided.

Authors:: Kolda, Tamara G.

Publication Date:: 2018-01-01

Research Org.:: Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

Sponsoring Org.:: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)

OSTI Identifier:: 1513637

Report Number(s):: SAND2018-0609C
660075

DOE Contract Number:: AC04-94AL85000

Resource Type:: Conference

Resource Relation:: Conference: Proposed for presentation at the Joint Mathematics Meetings held January 10-13, 2018 in San Diego, CA.

Country of Publication:: United States

Language:: English

Citation Formats


                    Kolda, Tamara G. Tensor Decomposition: A Mathematical Tool for Data Analysis..  United States: N. p., 2018. 
        Web.

Copy to clipboard


                    Kolda, Tamara G. Tensor Decomposition: A Mathematical Tool for Data Analysis..  United States.

Copy to clipboard


                    Kolda, Tamara G. Mon .  
        "Tensor Decomposition: A Mathematical Tool for Data Analysis.".  United States.  https://www.osti.gov/servlets/purl/1513637.

Copy to clipboard


                    
@article{osti_1513637,

  title        = {Tensor Decomposition: A Mathematical Tool for Data Analysis.},

  author       = {Kolda, Tamara G.},

  abstractNote = {Abstract not provided.},

  doi          = {},

  journal      = {},
number       = ,

  volume       = ,

  place        = {United States},

  year         = {2018},

  month        = {1}

}

Copy to clipboard

Conference:

View Conference (7.87 MB)

Other availability

Please see Document Availability for additional information on obtaining the full-text document. Library patrons may search WorldCat to identify libraries that hold this conference proceeding.

Save / Share:

Export Metadata

Save to My Library

Similar records in OSTI.GOV collections:

SVD + factor rotation : a powerful alternative to PCA in spectral image analysis.

Conference Keenan, Michael Robert

Factor analysis has proven an effective approach for distilling high dimensional spectral-image data into a limited number of components that describe the spatial and spectral characteristics of the imaged sample. Principal Component Analysis (PCA) is the most commonly used factor analysis tool; however, PCA constrains both the spectral and abundance factors to be orthogonal, and forces the components to serially maximize the variance that each accounts for. Neither constraint has any basis in physical reality; thus, principal components are abstract and not easily interpreted. The mathematical properties of PCA scores and loadings also differ subtly, which has implications for howmore »« less
Multilinear algebra for analyzing data with multiple linkages.

Conference Dunlavy, Daniel M. ; Kegelmeyer, W. Philip, Jr. ; Kolda, Tamara Gibson

Link analysis typically focuses on a single type of connection, e.g., two journal papers are linked because they are written by the same author. However, often we want to analyze data that has multiple linkages between objects, e.g., two papers may have the same keywords and one may cite the other. The goal of this paper is to show that multilinear algebra provides a tool for multilink analysis. We analyze five years of publication data from journals published by the Society for Industrial and Applied Mathematics. We explore how papers can be grouped in the context of multiple link typesmore »« less
An optimization approach for fitting canonical tensor decompositions.

Technical Report Dunlavy, Daniel M. ; Acar, Evrim ; Kolda, Tamara Gibson

Tensor decompositions are higher-order analogues of matrix decompositions and have proven to be powerful tools for data analysis. In particular, we are interested in the canonical tensor decomposition, otherwise known as the CANDECOMP/PARAFAC decomposition (CPD), which expresses a tensor as the sum of component rank-one tensors and is used in a multitude of applications such as chemometrics, signal processing, neuroscience, and web analysis. The task of computing the CPD, however, can be difficult. The typical approach is based on alternating least squares (ALS) optimization, which can be remarkably fast but is not very accurate. Previously, nonlinear least squares (NLS) methodsmore »« less
DOI: 10.2172/978916

Full Text Available
Genten: Software for Generalized Tensor Decompositions v. 1.0.0

Software Phipps, Eric T. ; Kolda, Tamara G. ; Dunlavy, Daniel ; ...

Tensors, or multidimensional arrays, are a powerful mathematical means of describing multiway data. This software provides computational means for decomposing or approximating a given tensor in terms of smaller tensors of lower dimension, focusing on decomposition of large, sparse tensors. These techniques have applications in many scientific areas, including signal processing, linear algebra, computer vision, numerical analysis, data mining, graph analysis, neuroscience and more. The software is designed to take advantage of parallelism present emerging computer architectures such has multi-core CPUs, many-core accelerators such as the Intel Xeon Phi, and computation-oriented GPUs to enable efficient processing of large tensors.
View Software
Analysis-based arguments for abstract data type calculus.

Conference Rouson, Damian W. I.

Increasing demands on the complexity of scientific models coupled with increasing demands for their scalability are placing programming models on equal footing with the numerical methods they implement in terms of significance. A recurring theme across several major scientific software development projects involves defining abstract data types (ADTs) that closely mimic mathematical abstractions such as scalar, vector, and tensor fields. In languages that support user-defined operators and/or overloading of intrinsic operators, coupling ADTs with a set of algebraic and/or integro-differential operators results in an ADT calculus. This talk will analyze ADT calculus using three tool sets: object-oriented design metrics, computationalmore »« less

Similar Records