A nonlinear neural network model of mixture of local principal component analysis: Application to handwritten digits recognition

DOI10.1016/S0031-3203(00)00009-1zbMath0991.68062MaRDI QIDQ5957580

Hong Yan, Bailing Zhang, Fu, Minyue

Publication date: 9 September 2002

Published in: Pattern Recognition (Search for Journal in Brave)

zbMATH Keywords

principal component analysis

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Pattern recognition, speech recognition (68T10)

Related Items (4)

Class-specific feature polynomial classifier for pattern classification and its application to handwritten numeral recognition ⋮ A concise functional neural network computing the largest modulus eigenvalues and their corresponding eigenvectors of a real skew matrix ⋮ A recurrent neural network computing the largest imaginary or real part of eigenvalues of real matrices ⋮ Robust extraction of local structures by the minimum \(\beta\)-divergence method

Cites Work

• A simplified neuron model as a principal component analyzer
• A deterministic annealing approach to clustering
• Principal Curves

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