Laboratory of Computer and Information Science > Teaching > T-122.102 > Literature

T-122.102 Literature

[Under construction.]

• "Oldies":
  • J measure, essentially the mutual information, and frequent sets:
    P. Smyth, R. M. Goodman, An information theoretic approach to rule induction from databases, IEEE Transactions on Knowledge and Data Engineering 4(4), 1992.
  • Maximum entropy methods, GIS:
    J. N. Darroch, D. Ratcliff, Generalized iterative scaling for log-linear models, The Annals of Mathematical Statistics 43(5) 1470-1480, 1972.
    J. Goodman, Sequential Conditional Generalized Iterative scaling, 2002. [CiteSeer]
• Generative models:
  • Generative model, solved with MCMC:
    J.K. Pritchard, M. Stephens, P. Donnelly, "Inference of Population Structure Using Multilocus Genotype Data", 2000. [PDF] [10.2.2004]
  • Correlations added:
    D. Falush, M. Stephens, J. K. Pritchard, Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies, 2003. [PDF]
  • Basics of the variational method (LDA/mPCA):
    D. M. Blei, A. Y. Ng, M. I. Jordan, Latent Dirichlet Allocation, 2002. [CiteSeer]
    W. Buntine, Variational extensions to EM and Multinomial PCA, 2002. [CiteSeer]
  • LDA ja annotations:
    D. M. Blei, M. I. Jordan, Modeling annotated data, 2003. [PDF]
  • Theoretical basis, a different viewpoint:
    M. Collins, S. Dasgupta, R. E. Schapire, A generalization of principal component analysis to the exponential family, 2001. [CiteSeer]
  • Logistic PCA:
    A. I. Schein, L. K. Saul, L. H. Ungar, A Generalized Linear Model for Principal Component Analysis of Binary Data, 2003. [CiteSeer]
• Mutual information related methods:
  • Basics:
    N. Tishby, F. C. Pereira, W. Bialek, The Information Bottleneck method, 1999. [CiteSeer]
  • IB applications, e.g.:
    N. Slonim, N. Friedman, N. Tishby, Unsupervised Document Classification using Sequential Information Maximixation, 2002. [CiteSeer]
    R. Bekkerman, R. El-Yaniv, N. Tishby, Y. Winter, Distributional word clusters vs. words for text categorization, 2003. [CiteSeer]
  • Projection functions:
    A. Globerson, N. Tishby, Sufficient dimensionality reduction, 2003. [PS]
  • Discriminative Clustering:
    J. Sinkkonen, S. Kaski, J. Nikkilä. Discriminative Clustering: Optimal Contingency Tables by Learning Metrics, 2002. [CiteSeer]
• Kernel methods:
  • Survey:
    T. Gärtner, A Survey of Kernels for Structured Data, 2002. [PDF] [3.2.2004]
  • Fischer kernels:
    T. S. Jaakkola, D. Haussler, Exploiting Generative Models in Discriminative Classifiers, 1998. [CiteSeer]
    T. Jaakkola, M. Diekhans, D. Haussler, A discriminative framework for detecting remote protein homologies, 1998. [CiteSeer]
  • Information geometry:
    J. Lafferty, G. Lebanon, Information diffusion kernels, 2002. [CiteSeer]
    [Additionally something of information geometry?]
  • Other approaches:
    M. Seeger, Covariance kernels from Bayesian generative models, 2001. [CiteSeer]
    K. Tsuda, T. Kin, K. Asai, Marginalized kernels for biological sequences, 2002. [PDF]
• Discretizing data:
  • H. Steck, T. S. Jaakkola, (Semi-)Predictive Discretization During Model Selection, 2003. [PS]
  • [To be added]