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A Compressive Sensing Model for Speeding Up Text Classification.

Kelin Shen1, Peinan Hao2,3, Ran Li2,3

  • 1School of Foreign Languages, Xinyang Agriculture and Forestry University, Xinyang 46400, China.

Computational Intelligence and Neuroscience
|August 25, 2020
PubMed
Summary

Compressive sensing (CS) accelerates text classification by reducing feature space, offering low complexity for big data. This method preserves feature distances, speeding up analysis with minimal accuracy loss.

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Area of Science:

  • Computer Science
  • Data Science
  • Machine Learning

Background:

  • Text classification is crucial for big data applications.
  • High dimensionality and sparsity of text features pose challenges to efficient classification.
  • Existing methods struggle with computational and memory limitations.

Purpose of the Study:

  • To propose a compressive sensing-based model to accelerate text classification.
  • To address the challenges of high dimensionality and sparsity in text data.
  • To develop an efficient text classification method with low time and space complexity.

Main Methods:

  • Utilizing compressive sensing (CS) to reduce the text feature space.
  • Employing structural random matrices (SRMs) for efficient random projections.
  • Ensuring the restricted isometry property (RIP) to preserve feature distances.

Main Results:

  • The CS-based model significantly speeds up text classification.
  • The model demonstrates low time and space complexity during training.
  • Pairwise distances between text features are well-preserved.
  • Structural random matrices overcome computational and memory limitations.

Conclusions:

  • Compressive sensing provides an effective solution for accelerating text classification in big data.
  • The proposed method achieves high efficiency without compromising classification accuracy.
  • CS, particularly with SRMs, offers a practical approach to dimensionality reduction for text data.