IEEE Transactions on Emerging Topics in Computing· 2014Q1
A Tensor-Based Approach for Big Data Representation and Dimensionality Reduction
- 178citations
- Q1SCImago
- 2014year
Short summary
A novel tensor model represents diverse big data types (structured, semi-structured, unstructured) as subtensors merged into a unified tensor, enabling efficient dimensionality reduction via an incremental high-order singular value decomposition (IHOSVD).
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Key points
- Proposes a unified tensor model to represent unstructured, semi-structured, and structured big data.
- Introduces an incremental high-order singular value decomposition (IHOSVD) for dimensionality reduction.
- IHOSVD recursively updates orthogonal bases to compute a new core tensor.
- Case study shows 93% accuracy with only 18% of core tensor elements.
AI-generated from the title and abstract; the full text is not read.
Abstract
Variety and veracity are two distinct characteristics of large-scale and heterogeneous data. It has been a great challenge to efficiently represent and process big data with a unified scheme. In this paper, a unified tensor model is proposed to represent the unstructured, semistructured, and structured data. With tensor extension operator, various types of data are represented as subtensors and then are merged to a unified tensor. In order to extract the core tensor which is small but contains valuable information, an incremental high order singular value decomposition (IHOSVD) method is presented. By recursively applying the incremental matrix decomposition algorithm, IHOSVD is able to update the orthogonal bases and compute the new core tensor. Analyzes in terms of time complexity, memory usage, and approximation accuracy of the proposed method are provided in this paper. A case study illustrates that approximate data reconstructed from the core set containing 18% elements can guarantee 93% accuracy in general. Theoretical analyzes and experimental results demonstrate that the proposed unified tensor model and IHOSVD method are efficient for big data representation and dimensionality reduction.
The authors' abstract, as published at the source. IEEE Transactions on Emerging Topics in Computing, 2014 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics