@InProceedings{Franc-GreedyKPCA-2006,
  IS = { zkontrolovano 29 Dec 2006 },
  UPDATE  = { 2006-07-17 },
  author =       {Franc, Vojt{\v e}ch and Hlav{\'a}{\v c}, V{\'a}clav},
  title =        {Greedy Kernel Principal Component Analysis},
  booktitle =    {Cognitive Vision Systems},
  pages =        {87--106},
  figures =      {5},
  year =         {2006},
  issn =         {0302-9743},
  isbn =         {3-540-33971-X},
  book_pages =   {365},
  venue =        {Dagstuhl Castle, Germany},
  month =        {February},
  editor =       {Christensen, Henrik I. and Nagel, Hans-Hellmut},
  publisher =    {Springer-Verlag},
  address =      {Heidelberg, Germany},
  autorship =    {90-10},
  project =      {MIRACLE ICA1-CT-2000-70002, IST-2001-32184 ActIPret, 
                  MSM6840770013, GACR 102/03/0440},
  annote = {This contribution discusses one aspect of statistical
    learning and generalization. Theory of learning is very relevant
    to cognitive systems including cognitive vision.  A technique
    allowing to approximate a huge training set is proposed. The
    approach aims to represent data in a low dimensional space with
    possibly minimal representation error which is similar to the
    Principal Component Analysis (PCA). In contrast to the PCA, the
    basis vectors of the low dimensional space used for data
    representation are properly selected vectors from the training set
    and not as their linear combinations. The basis vectors can be
    selected by a simple algorithm which has low computational
    requirements and allows on-line processing of huge data sets. As
    the computations in the proposed algorithm appear in a form of dot
    product, kernel methods can be used to cope with non-linear
    problems.  The proposed method was tested to approximate training
    sets of the Support Vector Machines and Kernel Fisher Linear
    Discriminant which are known method for learning classifiers. The
    experiments show that the proposed approximation can significantly
    reduce the complexity of the found classifiers while retaining
    their accuracy. On the other hand, the method is not very suitable
    for denoising. },
  keywords = {Principal Component Analysis, Kernel Methods},
}