71 research outputs found
Guaranteed clustering and biclustering via semidefinite programming
Identifying clusters of similar objects in data plays a significant role in a
wide range of applications. As a model problem for clustering, we consider the
densest k-disjoint-clique problem, whose goal is to identify the collection of
k disjoint cliques of a given weighted complete graph maximizing the sum of the
densities of the complete subgraphs induced by these cliques. In this paper, we
establish conditions ensuring exact recovery of the densest k cliques of a
given graph from the optimal solution of a particular semidefinite program. In
particular, the semidefinite relaxation is exact for input graphs corresponding
to data consisting of k large, distinct clusters and a smaller number of
outliers. This approach also yields a semidefinite relaxation for the
biclustering problem with similar recovery guarantees. Given a set of objects
and a set of features exhibited by these objects, biclustering seeks to
simultaneously group the objects and features according to their expression
levels. This problem may be posed as partitioning the nodes of a weighted
bipartite complete graph such that the sum of the densities of the resulting
bipartite complete subgraphs is maximized. As in our analysis of the densest
k-disjoint-clique problem, we show that the correct partition of the objects
and features can be recovered from the optimal solution of a semidefinite
program in the case that the given data consists of several disjoint sets of
objects exhibiting similar features. Empirical evidence from numerical
experiments supporting these theoretical guarantees is also provided
Tightness of the maximum likelihood semidefinite relaxation for angular synchronization
Maximum likelihood estimation problems are, in general, intractable
optimization problems. As a result, it is common to approximate the maximum
likelihood estimator (MLE) using convex relaxations. In some cases, the
relaxation is tight: it recovers the true MLE. Most tightness proofs only apply
to situations where the MLE exactly recovers a planted solution (known to the
analyst). It is then sufficient to establish that the optimality conditions
hold at the planted signal. In this paper, we study an estimation problem
(angular synchronization) for which the MLE is not a simple function of the
planted solution, yet for which the convex relaxation is tight. To establish
tightness in this context, the proof is less direct because the point at which
to verify optimality conditions is not known explicitly.
Angular synchronization consists in estimating a collection of phases,
given noisy measurements of the pairwise relative phases. The MLE for angular
synchronization is the solution of a (hard) non-bipartite Grothendieck problem
over the complex numbers. We consider a stochastic model for the data: a
planted signal (that is, a ground truth set of phases) is corrupted with
non-adversarial random noise. Even though the MLE does not coincide with the
planted signal, we show that the classical semidefinite relaxation for it is
tight, with high probability. This holds even for high levels of noise.Comment: 2 figure
Exact Clustering of Weighted Graphs via Semidefinite Programming
As a model problem for clustering, we consider the densest k-disjoint-clique
problem of partitioning a weighted complete graph into k disjoint subgraphs
such that the sum of the densities of these subgraphs is maximized. We
establish that such subgraphs can be recovered from the solution of a
particular semidefinite relaxation with high probability if the input graph is
sampled from a distribution of clusterable graphs. Specifically, the
semidefinite relaxation is exact if the graph consists of k large disjoint
subgraphs, corresponding to clusters, with weight concentrated within these
subgraphs, plus a moderate number of outliers. Further, we establish that if
noise is weakly obscuring these clusters, i.e, the between-cluster edges are
assigned very small weights, then we can recover significantly smaller
clusters. For example, we show that in approximately sparse graphs, where the
between-cluster weights tend to zero as the size n of the graph tends to
infinity, we can recover clusters of size polylogarithmic in n. Empirical
evidence from numerical simulations is also provided to support these
theoretical phase transitions to perfect recovery of the cluster structure
Relax, no need to round: integrality of clustering formulations
We study exact recovery conditions for convex relaxations of point cloud
clustering problems, focusing on two of the most common optimization problems
for unsupervised clustering: -means and -median clustering. Motivations
for focusing on convex relaxations are: (a) they come with a certificate of
optimality, and (b) they are generic tools which are relatively parameter-free,
not tailored to specific assumptions over the input. More precisely, we
consider the distributional setting where there are clusters in
and data from each cluster consists of points sampled from a
symmetric distribution within a ball of unit radius. We ask: what is the
minimal separation distance between cluster centers needed for convex
relaxations to exactly recover these clusters as the optimal integral
solution? For the -median linear programming relaxation we show a tight
bound: exact recovery is obtained given arbitrarily small pairwise separation
between the balls. In other words, the pairwise center
separation is . Under the same distributional model, the
-means LP relaxation fails to recover such clusters at separation as large
as . Yet, if we enforce PSD constraints on the -means LP, we get
exact cluster recovery at center separation .
In contrast, common heuristics such as Lloyd's algorithm (a.k.a. the -means
algorithm) can fail to recover clusters in this setting; even with arbitrarily
large cluster separation, k-means++ with overseeding by any constant factor
fails with high probability at exact cluster recovery. To complement the
theoretical analysis, we provide an experimental study of the recovery
guarantees for these various methods, and discuss several open problems which
these experiments suggest.Comment: 30 pages, ITCS 201
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