Comparison Of Linear Dimensionality Reduction Methods On Classification Methods

YILDIZ E., SEVİM Y.

National Conference on Electrical, Electronics and Biomedical Engineering (ELECO), Bursa, Türkiye, 1 - 03 Aralık 2016, ss.161-164 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Basıldığı Şehir: Bursa
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.161-164
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

The analysis of high dimensional data is encountered in many areas. In the analysis of that kind of data, dimensionality reduction methods are used to work with fewer dimensions. Generally, linear dimensionality reduction methods are more preferred. In this paper, popular linear dimensionality reduction methods and their performance are investigated. These methods are principal component analysis (PCA), linear discriminant analysis (LDA), locality preserving projection (LPP), neighborhood preserving embedding (NPE) and locality sensitive discriminant analysis (LSDA).