Applications of singular value decomposition in data reduction

The singular value decomposition (SVD) is broadly used in the dimensional reduction due to its universality for all matrices. We have deduced the mathematical formula of the SVD and logical thinking of SVD truncation. Dimensional reduction just uses the SVD truncation to select N biggest singular values. We have experimentally found that image compression used by SVD could greatly save memory without losing too much accuracy. The effectiveness of SVD could be directly shown in image recognition with complex data hierarchy. We also combined SVD with another useful dimensional reduction method named Principle Component Analysis (PCA). Our analysis indicates that the accuracy of both SVD and PCA could be guaranteed when processing a large number of data. Taken together with the data science and machine learning, SVD appears to be a fundamental tool of data processing. Our results establish a basic understanding of SVD in the application of dimensional reduction.

• Publication year

  2022

• Venue

  Other Conferences

• Publication date

  2022-09-27

• Fields of study

  Mathematics, Computer Science, Engineering

• Identifiers
  DOI 10.1117/12.2648696
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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