Incremental tensor principal component analysis for handwritten digit recognition

DOI10.1155/2014/819758zbMath1407.68426OpenAlexW2094600769WikidataQ59069585 ScholiaQ59069585MaRDI QIDQ1719207

Tao Yan, Chang Liu, Feng Lin, Dan Li, Yong Hong Liu, Jiliu Zhou, Weidong Zhao

Publication date: 8 February 2019

Published in: Mathematical Problems in Engineering (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1155/2014/819758

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Pattern recognition, speech recognition (68T10)

Related Items (1)

A MRI denoising method based on 3D nonlocal means and multidimensional PCA

Cites Work

• A survey of multilinear subspace learning for tensor data
• Extracting the optimal dimensionality for local tensor discriminant analysis
• Face recognition using discriminant locality preserving projections based on maximum margin criterion
• From image vector to matrix: A straightforward image projection technique -- IMPCA vs. PCA
• Generalized low rank approximations of matrices

This page was built for publication: Incremental tensor principal component analysis for handwritten digit recognition