103 citations · 268 across the 27 of their papers we have counts for
8 papers · 1 filter
Dictionary-based Manifold Learning
Hanyu Zhang, Samson Koelle, Marina Meila
We propose a paradigm for interpretable Manifold Learning for scientific data analysis, whereby we parametrize a manifold with smooth functions from a scientist-provided dictio…
megaman: Manifold Learning with Millions of points
James McQueen, Marina Meila, Jacob VanderPlas +1
Manifold Learning is a class of algorithms seeking a low-dimensional non-linear representation of high-dimensional data. Thus manifold learning algorithms are, at least in theory,…
Graph Sensitive Indices for Comparing Clusterings
Zaeem Hussain, Marina Meila
This report discusses two new indices for comparing clusterings of a set of points. The motivation for looking at new ways for comparing clusterings stems from the fact that the ex…
An Experimental Comparison of Several Clustering and Initialization Methods
Marina Meila, David Heckerman
We examine methods for clustering in high dimensions. In the first part of the paper, we perform an experimental comparison between three batch clustering algorithms: the Expectati…
Unsupervised spectral learning
Susan Shortreed, Marina Meila
In spectral clustering and spectral image segmentation, the data is partioned starting from a given matrix of pairwise similarities S. the matrix S is constructed by hand, or learn…
Consensus ranking under the exponential model
Marina Meila, Kapil Phadnis, Arthur Patterson +1
We analyze the generalized Mallows model, a popular exponential model over rankings. Estimating the central (or consensus) ranking from data is NP-hard. We obtain the following new…
Estimation and Clustering with Infinite Rankings
Marina Meila, Le Bao
This paper presents a natural extension of stagewise ranking to the the case of infinitely many items. We introduce the infinite generalized Mallows model (IGM), describe its prope…
Dirichlet Process Mixtures of Generalized Mallows Models
Marina Meila, Harr Chen
We present a Dirichlet process mixture model over discrete incomplete rankings and study two Gibbs sampling inference techniques for estimating posterior clusterings. The first app…