2,201 research outputs found
Chordal Decomposition in Rank Minimized Semidefinite Programs with Applications to Subspace Clustering
Semidefinite programs (SDPs) often arise in relaxations of some NP-hard
problems, and if the solution of the SDP obeys certain rank constraints, the
relaxation will be tight. Decomposition methods based on chordal sparsity have
already been applied to speed up the solution of sparse SDPs, but methods for
dealing with rank constraints are underdeveloped. This paper leverages a
minimum rank completion result to decompose the rank constraint on a single
large matrix into multiple rank constraints on a set of smaller matrices. The
re-weighted heuristic is used as a proxy for rank, and the specific form of the
heuristic preserves the sparsity pattern between iterations. Implementations of
rank-minimized SDPs through interior-point and first-order algorithms are
discussed. The problem of subspace clustering is used to demonstrate the
computational improvement of the proposed method.Comment: 6 pages, 6 figure
Reducing Dueling Bandits to Cardinal Bandits
We present algorithms for reducing the Dueling Bandits problem to the
conventional (stochastic) Multi-Armed Bandits problem. The Dueling Bandits
problem is an online model of learning with ordinal feedback of the form "A is
preferred to B" (as opposed to cardinal feedback like "A has value 2.5"),
giving it wide applicability in learning from implicit user feedback and
revealed and stated preferences. In contrast to existing algorithms for the
Dueling Bandits problem, our reductions -- named \Doubler, \MultiSbm and
\DoubleSbm -- provide a generic schema for translating the extensive body of
known results about conventional Multi-Armed Bandit algorithms to the Dueling
Bandits setting. For \Doubler and \MultiSbm we prove regret upper bounds in
both finite and infinite settings, and conjecture about the performance of
\DoubleSbm which empirically outperforms the other two as well as previous
algorithms in our experiments. In addition, we provide the first almost optimal
regret bound in terms of second order terms, such as the differences between
the values of the arms
Similarity Learning for High-Dimensional Sparse Data
A good measure of similarity between data points is crucial to many tasks in
machine learning. Similarity and metric learning methods learn such measures
automatically from data, but they do not scale well respect to the
dimensionality of the data. In this paper, we propose a method that can learn
efficiently similarity measure from high-dimensional sparse data. The core idea
is to parameterize the similarity measure as a convex combination of rank-one
matrices with specific sparsity structures. The parameters are then optimized
with an approximate Frank-Wolfe procedure to maximally satisfy relative
similarity constraints on the training data. Our algorithm greedily
incorporates one pair of features at a time into the similarity measure,
providing an efficient way to control the number of active features and thus
reduce overfitting. It enjoys very appealing convergence guarantees and its
time and memory complexity depends on the sparsity of the data instead of the
dimension of the feature space. Our experiments on real-world high-dimensional
datasets demonstrate its potential for classification, dimensionality reduction
and data exploration.Comment: 14 pages. Proceedings of the 18th International Conference on
Artificial Intelligence and Statistics (AISTATS 2015). Matlab code:
https://github.com/bellet/HDS
Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams
The name entity disambiguation task aims to partition the records of multiple
real-life persons so that each partition contains records pertaining to a
unique person. Most of the existing solutions for this task operate in a batch
mode, where all records to be disambiguated are initially available to the
algorithm. However, more realistic settings require that the name
disambiguation task be performed in an online fashion, in addition to, being
able to identify records of new ambiguous entities having no preexisting
records. In this work, we propose a Bayesian non-exhaustive classification
framework for solving online name disambiguation task. Our proposed method uses
a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model
which enables identification of new ambiguous entities who have no records in
the training data. For online classification, we use one sweep Gibbs sampler
which is very efficient and effective. As a case study we consider
bibliographic data in a temporal stream format and disambiguate authors by
partitioning their papers into homogeneous groups. Our experimental results
demonstrate that the proposed method is better than existing methods for
performing online name disambiguation task.Comment: to appear in CIKM 201
Highly Scalable Algorithms for Robust String Barcoding
String barcoding is a recently introduced technique for genomic-based
identification of microorganisms. In this paper we describe the engineering of
highly scalable algorithms for robust string barcoding. Our methods enable
distinguisher selection based on whole genomic sequences of hundreds of
microorganisms of up to bacterial size on a well-equipped workstation, and can
be easily parallelized to further extend the applicability range to thousands
of bacterial size genomes. Experimental results on both randomly generated and
NCBI genomic data show that whole-genome based selection results in a number of
distinguishers nearly matching the information theoretic lower bounds for the
problem
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