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- Marina Meilă
- 2011

This paper proposes an information theoretic criterion for comparing two partitions, or clusterings, of the same data set. The criterion, called variation of information (VI), measures the amount of information lost and gained in changing from clustering C to clustering C′. The basic properties of VI are presented and discussed. We focus on two kinds of… (More)

- Marina Meila, Jianbo Shi
- AISTATS
- 2001

We present a new view of clustering and segmentation by pairwise similarities. We interpret the similarities as edge ows in a Markov random walk and study the eigenvalues and eigenvectors of the walk's transition matrix. This view shows that spectral methods for clustering and segmentation have a probabilistic foundation. We prove that the Normalized Cut… (More)

- Marina Meila
- ICML
- 2005

This paper views clusterings as elements of a lattice. Distances between clusterings are analyzed in their relationship to the lattice. From this vantage point, we first give an axiomatic characterization of some criteria for comparing clusterings, including the variation of information and the unadjusted Rand index. Then we study other distances between… (More)

- Tommi S. Jaakkola, Marina Meila, Tony Jebara
- NIPS
- 1999

Tony Jebara MIT Media Lab 20 Ames St. Cambridge, MA 02139 jebara@media. mit. edu We present a general framework for discriminative estimation based on the maximum entropy principle and its extensions. All calculations involve distributions over structures and/or parameters rather than specific settings and reduce to relative entropy projections. This holds… (More)

- Marina Meila, Michael I. Jordan
- Journal of Machine Learning Research
- 1999

One of the challenges of density estimation as it is used in machine learning is that usually the data are multivariate and often the dimensionality is large. Operating with joint distributions over multidimensional domains raises specific problems that are not encountered in the univariate case. Graphical models are representations of joint densities that… (More)

- Marina Meila, Jianbo Shi
- NIPS
- 2000

We present a new view of image segmentation by pairwise similarities. We interpret the similarities as edge ows in a Markov random walk and study the eigenvalues and eigenvectors of the walk's transition matrix. This interpretation shows that spectral methods for clustering and segmentation have a probabilistic foundation. In particular, we prove that the… (More)

- Marina Meila
- COLT
- 2003

- Marina Meila, Tommi S. Jaakkola
- UAI
- 2000

In this paper we present decomposable priors, a family of priors over structure and parameters of tree belief nets for which Bayesian learning with complete observations is tractable, in the sense that the posterior is also decomposable and can be completely determined ana lytically in polynomial time. This fol lows from two main results: First, we show… (More)

- Anne Patrikainen, Marina Meila
- IEEE Transactions on Knowledge and Data…
- 2006

We present the first framework for comparing subspace clusterings. We propose several distance measures for subspace clusterings, including generalizations of well-known distance measures for ordinary clusterings. We describe a set of important properties for any measure for comparing subspace clusterings and give a systematic comparison of our proposed… (More)

- Tommi S. Jaakkola, David Sontag, Amir Globerson, Marina Meila
- AISTATS
- 2010

We propose to solve the combinatorial problem of finding the highest scoring Bayesian network structure from data. This structure learning problem can be viewed as an inference problem where the variables specify the choice of parents for each node in the graph. The key combinatorial difficulty arises from the global constraint that the graph structure has… (More)