@inproceedings{5514,
  title = {Iterative Subgraph Mining for Principal Component Analysis},
  journal = {Proceedings of the IEEE International Conference on Data Mining (ICDM 2008)},
  booktitle = {ICDM 2008},
  abstract = {Graph mining methods enumerate frequent subgraphs
  efficiently, but they are not necessarily good features for
  machine learning due to high correlation among features.
  Thus it makes sense to perform principal component analysis
  to reduce the dimensionality and create decorrelated
  features. We present a novel iterative mining algorithm
  that captures informative patterns corresponding to major
  entries of top principal components. It repeatedly calls
  weighted substructure mining where example weights are
  updated in each iteration. The Lanczos algorithm, a standard
  algorithm of eigendecomposition, is employed to update
  the weights. In experiments, our patterns are shown to
  approximate the principal components obtained by frequent
  mining.},
  pages = {1007-1012},
  editors = {Giannotti, F. , D. Gunopulos, F. Turini, C. Zaniolo, N. Ramakrishnan, X. Wu},
  publisher = {IEEE Computer Society},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  address = {Los Alamitos, CA, USA},
  month = dec,
  year = {2008},
  author = {Saigo, H. and Tsuda, K.},
  doi = {10.1109/ICDM.2008.62},
  month_numeric = {12}
}