Incremental feature extraction based on decision boundaries

Seongyoun Woo, Chul Hee Lee

Research output: Contribution to journalArticle

5 Citations (Scopus)

Abstract

Feature extraction is a key algorithm to solve the dimensionality problem. Most feature extraction algorithms use a batch mode, which requires all data available at the same time to calculate new features. Recently, with more available data and advancements of transmission technology, the need for incremental algorithms has increased. In this paper, we propose a gradient descent DBFE method (GDDBFE) that shows a substantial improvement in processing time. Based on this GDDBFE, we then propose an incremental gradient descent decision boundary feature extraction method (IGDDBFE). The proposed IGDDBFE method consists of two steps: updating the decision boundaries and adding discriminately informative features with newly added samples and then updating the feature vectors by incremental eigenvector updates. Experiments with real-world databases show that the proposed method shows improved performance compared to some existing methods.

Original languageEnglish
Pages (from-to)65-74
Number of pages10
JournalPattern Recognition
Volume77
DOIs
Publication statusPublished - 2018 May 1

Fingerprint

Feature extraction
Eigenvalues and eigenfunctions
Processing
Experiments

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

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Incremental feature extraction based on decision boundaries. / Woo, Seongyoun; Lee, Chul Hee.

In: Pattern Recognition, Vol. 77, 01.05.2018, p. 65-74.

Research output: Contribution to journalArticle

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