An approach to non-linear principal components analysis using radially symmetric kernel functions

NON-LINEAR GENERALIZATION OF PRINCIPAL C

NON-LINEAR GENERALIZATION OF PRINCIPAL COMPONENT ANALYSIS: FROM A GLOBAL TO A LOCAL APPROACH

✍ G. KERSCHEN; J.-C. GOLINVAL 📂 Article 📅 2002 🏛 Elsevier Science 🌐 English ⚖ 288 KB

Principal component analysis (PCA), also known as proper orthogonal decomposition or Karhunen}Loe`ve transform, is commonly used to reduce the dimensionality of a data set with a large number of interdependent variables. PCA is the optimal linear transformation with respect to minimizing the mean sq