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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)

a grant from Darpa in support of the CALO program. The authors wish to acknowledge helpful discussions with Lancelot James and Jim Pitman and the referees for useful comments. Abstract 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… (More)

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

Yair Weiss School of CS & Engr. The Hebrew Univ. Despite many empirical successes of spectral clustering methods-algorithms that cluster points using eigenvectors of matrices derived from the data-there are several unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in slightly different ways. Second, many of these… (More)

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

- 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)

- Zoubin Ghahramani, Michael I. Jordan
- NIPS
- 1995

We present a framework for learning in hidden Markov models with distributed state representations. Within this framework , we derive a learning algorithm based on the Expectation-Maximization (EM) procedure for maximum likelihood estimation. Analogous to the standard Baum-Welch update rules, the M-step of our algorithm is exact and can be solved… (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 Expectation… (More)

- David M. Blei, Michael I. Jordan
- SIGIR
- 2003

We consider the problem of modeling annotated data---data with multiple types where the instance of one type (such as a caption) serves as a description of the other type (such as an image). We describe three hierarchical probabilistic mixture models which aim to describe such data, culminating in <i>correspondence latent Dirichlet allocation</i>, a latent… (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)