IEEE Journal of Selected Topics in Signal Processing· 2015Q1
Structured Data Fusion
- 220citations
- Q1SCImago
- 2015year
Short summary
Structured Data Fusion (SDF) is a new framework for knowledge discovery from multiple, potentially incomplete datasets by factorizing them with coupled tensor decompositions.
AI-generated from the title and abstract; the full text is not read.
Key points
- SDF allows knowledge discovery from multiple, possibly incomplete datasets.
- Datasets are represented as tensors and factorized using matrix/tensor decompositions.
- Factorizations can be coupled by sharing factors between datasets.
- Factors can be constrained by explicit functions of underlying variables.
- SDF can compute standard matrix factorizations like SVD and eigenvalue decomposition.
AI-generated from the title and abstract; the full text is not read.
Abstract
We present structured data fusion (SDF) as a framework for the rapid prototyping of knowledge discovery in one or more possibly incomplete data sets. In SDF, each data set-stored as a dense, sparse, or incomplete tensor-is factorized with a matrix or tensor decomposition. Factorizations can be coupled, or fused, with each other by indicating which factors should be shared between data sets. At the same time, factors may be imposed to have any type of structure that can be constructed as an explicit function of some underlying variables. With the right choice of decomposition type and factor structure, even well-known matrix factorizations such as the eigenvalue decomposition, singular value decomposition and QR factorization can be computed with SDF. A domain specific language (DSL) for SDF is implemented as part of the software package Tensorlab, with which we offer a library of tensor decompositions and factor structures to choose from. The versatility of the SDF framework is demonstrated by means of four diverse applications, which are all solved entirely within Tensorlab's DSL.
The authors' abstract, as published at the source. IEEE Journal of Selected Topics in Signal Processing, 2015 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics