· 2012
GigaTensor
- 246citations
- 2012year
Short summary
GigaTensor is a novel algorithm designed to perform tensor decomposition on massive datasets, overcoming the scalability limitations of existing methods.
AI-generated from the title and abstract; the full text is not read.
Key points
- Current tensor decomposition algorithms are not scalable for large tensors.
- The largest tensor decomposed in literature is thousands of sizes and hundreds of thousands of nonzeros.
- GigaTensor enables scalable decomposition for tensors with billions of entries and hundreds of millions of nonzeros.
AI-generated from the title and abstract; the full text is not read.
Abstract
Many data are modeled as tensors, or multi dimensional arrays. Examples include the predicates (subject, verb, object) in knowledge bases, hyperlinks and anchor texts in the Web graphs, sensor streams (time, location, and type), social networks over time, and DBLP conference-author-keyword relations. Tensor decomposition is an important data mining tool with various applications including clustering, trend detection, and anomaly detection. However, current tensor decomposition algorithms are not scalable for large tensors with billions of sizes and hundreds millions of nonzeros: the largest tensor in the literature remains thousands of sizes and hundreds thousands of nonzeros.
The authors' abstract, as published at the source., 2012 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics