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Fast, Linear Time Hierarchical Clustering using the Baire Metric
Fast Linear Time Hierarchical Clustering using Baire Metric
2011/7/5
The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm.
Methods of Hierarchical Clustering
Hierarchical Clustering hierarchical grid-based algorithm hierarchical density-based approaches
2011/6/20
We survey agglomerative hierarchical clustering algorithms and dis-
cuss efficient implementations that are available in R and other software
environments. We look at hierarchical self-organizing ma...
Multiway Spectral Clustering: A Margin-Based Perspective
Spectral clustering spectral relaxation graph partitioning reproducing kernel Hilbert space large-margin classifi ca-tion Gaussian intrinsic autoregression
2011/3/22
Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector problem, and in whi...
Multiway Spectral Clustering: A Margin-Based Perspective
Spectral clustering spectral relaxation graph partitioning reproducing kernel Hilbert space large-margin classifi ca-tion Gaussian intrinsic autoregression
2011/3/23
Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector problem, and in whi...
Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities
Active Clustering Robust and Efficient Hierarchical Clustering Adaptively Selected Similarities
2011/3/25
Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similaritie...
How the result of graph clustering methods depends on the construction of the graph
graph clustering construction
2011/3/21
We study the scenario of graph-based clustering algorithms such as spectral clustering. Given a set of data points, one first has to construct a graph on the data points and then apply a graph cluste...
Non-Gaussian gravitational clustering field statistics
Cosmology and Extragalactic Astrophysics (astro-ph.CO) Statistics Theory (math.ST)
2010/12/17
In this work we investigate the multivariate statistical description of the matter distribution in the nonlinear regime. We introduce the multivariate Edgeworth expansion of the lognormal distribution...
An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA
Learning (cs.LG) Optimization and Control (math.OC) Machine Learning (stat.ML)
2010/12/17
Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding cri...
Operator norm convergence of spectral clustering on level sets
Spectral clustering graph unsupervised classification levelsets connected components
2010/3/10
Following Hartigan [1975], a cluster is defined as a connected component of
the t-level set of the underlying density, i.e., the set of points for which the
density is greater than t. A clustering a...
A new penalized criterion for variable selection and clustering using genotypic data
Variables selection Penalized Likelihood Slope heuristics Mixture multinomial models Population genetics
2010/3/10
We consider the problem of estimating the number of components and the rel-
evant variables in a mixture model for multilocus genotypic data. A new pe-
nalized maximum likelihood criterion is propos...
Spectral clustering based on local linear approximations
Spectral Clustering Higher-Order Affinities Local Linear Approximation Local PolynomialApproximation
2010/3/9
In the context of clustering, we assume a generative model where each cluster is the result
of sampling points in the neighborhood of an embedded smooth surface, possibly contaminated
with outliers....
Efficient Utility-based Clustering over High Dimensional Partition Spaces
Efficient Utility-based Clustering High Dimensional Partition Spaces
2009/9/24
Because of the huge number of partitions of even a moderately sized dataset, even when Bayes factors have a closed form, in model-based clustering a comprehensive search for the highest sco...
Improved Criteria for Clustering Based on the Posterior Similarity Matrix
adjusted Rand index cluster analysis Dirichlet process mixture model Markov chain Monte Carlo
2009/9/24
In this paper we address the problem of obtaining a single clustering estimate c based on an MCMC sample of clusterings c(1), c(2) . . . , c(M) from the posterior distribution of a Bayesi...
Modal Clustering in a Class of Product Partition Models
Bayesian nonparametrics Dirichlet process mixture model maximum a posteriori clustering maximum likelihood clustering
2009/9/24
This paper defines a class of univariate product partition models for
which a novel deterministic search algorithm is guaranteed to find the maximum
aposteriori (MAP)clustering or the maximum likeli...
Model-based subspace clustering
COSA Dirichlet process mixture model nonparametric Bayes unsupervised learning variable selection
2009/9/21
We discuss a model-based approach to identifying clusters of objects
based on subsets of attributes, so that the attributes that distinguish a cluster
from the rest of the population may depend on t...