A least-squares framework for component analysis. De la Torre, F. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(6):1041 -1055, jun, 2012.
doi  bibtex   
  author = {De la Torre, F.},
 journal = {Pattern Analysis and Machine Intelligence, IEEE Transactions on},
 month = {jun},
 number = {6},
 pages = {1041 -1055},
 title = {A least-squares framework for component analysis},
 volume = {34},
 year = {2012},
 issn = {0162-8828},
 keywords = {CA techniques;canonical correlation analysis;classification;data nonlinear representation learning;eigen-formulation;eigen-problems;feature extraction step;least-squares framework;least-squares weighted kernel reduced rank regression;linear discriminant analysis;locality preserving projections;modeling;normalization factor intuitive interpretation lackness;numerical schemes;principal component analysis;rank deficient matrices;small sample size problem;spectral clustering;visualization;correlation methods;data visualisation;feature extraction;learning (artificial intelligence);least squares approximations;matrix algebra;pattern classification;pattern clustering;principal component analysis;regression analysis;},
 doi = {10.1109/TPAMI.2011.184},
 title_with_no_special_chars = {A LeastSquares Framework for Component Analysis}

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