12.4 reference

12.4.1 paper

毎回　参考文献めくってググるの面倒なので一覧にしてみた。
リンク貼るのは疲れたので途中でやめました（苦笑）

以下の一覧は論文のpdfを直接リンクしています。

一部、pdf以外のリンクもあります。

• Cardoso,1998
  • blind signal separation

• MacKay,2003
  • Information Theory, Inference and Learning Algorithms

• Bell and Sejnowski,1995
  • An information maximization approach to blind separation and blind deconvolution

• Attias,1999a
  • Independent factor analysis

• Jutten and Herault,1991
  • Blind separation of sources, Part 1: an adaptive algorithm based on neuromimetic architecture

• Comon et al., 1991
  • Blind source Separation, 2: problems statement.

• Amari et al., 1996
  • A new learning algorithm for blind signal separation.

• Pearlmutter and Parra, 1997
  • Maximum likelihood source separation

• Hyvarinen and Oja,1997
  • A fast fixed-point algorithm for independent component analysis.

• Hinton et al., 2001
  • A new view of ICA. In Proceedings of the International Conference on Independent Component Analysis and Blind Signal Separation, Volume 3.

• Miskin and MacKay., 2001
  • Ensemble learning for blind source separation.

• Hojen-Sorensen et al., 2002
  • Mean field approaches to independent component analysis.

• Choudrey and Roberts,2003
  • Variational mixture of Bayesian independent component analyzers.

• Chan et al.,2003
  • Variational Bayesian learning of ICA with missing data.

• Stone,2004
  • Independent Component Analysis

12.4.2

• Bourlard and Kamp,1988
  • Auto-association by multilayer perceptrons and singular value decomposition.

• Baldi and Hornik,1989
  • Neural networks and principal component analysis: learning from examples witout local minima.

12.4.3

• Kambhatla and Leen,1997
  • Dimension reduction by local principal component analysis.

• Hinton et al.,1997
  • Modelling the manifolds of images of handwritten digits.

• Tipping and Bishop,1999a
  • Mixtures of probabilistic principal component analyzers.

• Bishop and Winn,2000
  • Non linear Bayesian image modelling.

• Ghahramani and Hinton,1996a
  • The EM algorithm for mixtures of factor analyzers.

• Ghahramani and Beal,2000
  • Variational inference for Bayesian mixtues of factor analyzers.

• Bishop and Tipping,1998
  • A hierarchical latent variable model for data visualization.

• Hastie and Stuetzle,1989
  • Principal curves.

• Cox and Cox,2000
  • Multidimensional Scaling (Second ed.).

• Roweis and Saul,2000
  • Nonlinear dimensionality reduction by locally linear embedding.

• Tenenbaum et al.,2000
  • A global framework for nonlinear dimensionality resuction.

• Bartholomew,1987
  • Latent Variable Models and Factor Analysis.

• Tipping(1999)
  • Probabilistic visualisation of high-dimensional binary data.

• Collins et.al.(2002)
  • A generalization of principal component analysis to the exponential family.

• MacKay,1995
  • Bayesian neural networks and density networks.

• MacKay and Gibbs,1999
  • Density networks.

• Bishop et al.,1996
  • EM optimization of latent variable density models.

• Bishop et al.,1997a
  • GTM: a principled alternative to the Self-Organizing Map.

• Bishop et al.,1998b
  • GTM: the Generative Topographic Mapping.

• Kohonen,1982
  • Self-organized formation of topologically correct feature maps.

• Kohonen,1995
  • Self-organized Maps.

• Erwin et al.,1992
  • Self-organizing maps: ordering, convergence properties and energy functions.

• Tino and Nabney,2002
  • Hierarchical GTM: constructing localized non-linear projection manifolds in a principled way.

• Bishop et al.,1998a
  • Developments of the Generative Topographic Mapping.

• Bishop et al.,1997b
  • Magnification factors for the GTM algorithm.

• Tino et al.,2001
  • Using directional curvatures to visualize folding patterns of the GTM projection manifolds.

こうしてみると、参考文献多いなー。