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- David M. Blei, Andrew Y. Ng, Michael I. Jordan
- Journal of Machine Learning Research
- 2003

We propose a generative model for text and other collections of discrete data that generalizes or improves on several previous models including naive Bayes/unigram, mixture of unigrams [6], and Hofmann's aspect model , also known as probabilistic latent semantic indexing (pLSI) [3]. In the context of text modeling, our model posits that each document is… (More)

We consider problems involving groups of data, where each observation within a group is a draw from a mixture model, and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this setting it is natural to consider sets of Dirichlet… (More)

- Andrew Y. Ng, Michael I. Jordan, Yair Weiss
- NIPS
- 2001

Yair Weiss School of CS & Engr. The Hebrew Univ. yweiss@cs.huji.ac.il Despite many empirical successes of spectral clustering methodsalgorithms that cluster points using eigenvectors of matrices derived from the datathere are several unresolved issues. First , there are a wide variety of algorithms that use the eigenvectors in slightly different ways.… (More)

- Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan
- Journal of Machine Learning Research
- 2002

Kernel-based learning algorithms work by embedding the data into a Euclidean space, and then searching for linear relations among the embedded data points. The embedding is performed implicitly, by specifying the inner products between each pair of points in the embedding space. This information is contained in the so-called kernel matrix, a symmetric and… (More)

Many algorithms rely critically on being given a good metric over their inputs. For instance, data can often be clustered in many “plausible” ways, and if a clustering algorithm such as K-means initially fails to find one that is meaningful to a user, the only recourse may be for the user to manually tweak the metric until sufficiently good clusters are… (More)

- Michael I. Jordan, Robert A. Jacobs
- Neural Computation
- 1994

We present a tree-structured architecture for supervised learning. The statistical model underlying the architecture is a hierarchical mixture model in which both the mixture coefficients and the mixture components are generalized linear models (GLIM’s). Learning is treated as a maximum likelihood problem; in particular, we present an… (More)

- Martin J. Wainwright, Michael I. Jordan
- Foundations and Trends in Machine Learning
- 2008

The formalism of probabilistic graphical models provides a unifying framework for capturing complex dependencies among random variables, and building large-scale multivariate statistical models. Graphical models have become a focus of research in many statistical, computational and mathematical fields, including bioinformatics, communication theory,… (More)

- Francis R. Bach, Michael I. Jordan
- ICASSP
- 2002

We present a class of algorithms for independent component analysis (ICA) which use contrast functions based on canonical correlations in a reproducing kernel Hilbert space. On the one hand, we show that our contrast functions are related to mutual information and have desirable mathematical properties as measures of statistical dependence. On the other… (More)

While classical kernel-based classifiers are based on a single kernel, in practice it is often desirable to base classifiers on combinations of multiple kernels. Lanckriet et al. (2004) considered conic combinations of kernel matrices for the support vector machine (SVM), and showed that the optimization of the coefficients of such a combination reduces to… (More)

- Robert A. Jacobs, Michael I. Jordan, Steven J. Nowlan, Geoffrey E. Hinton
- Neural Computation
- 1991

We present a new supervised learning procedure for systems composed of many separate networks, each of which learns to handle a subset of the complete set of training cases. The new procedure can be viewed either as a modular version of a multilayer supervised network, or as an associative version of competitive learning. It therefore provides a new link… (More)