Inhalt

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   1 Introduction
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   1.1 State of the art
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   1.2 Outline and main results
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   1.3 Bibliographic notes
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   2 Bregman divergences
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   2.1 Definition
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   2.1.1 Basic properties
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   2.1.2 Examples of Bregman divergences
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  2.2 μ-similarity
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   2.2.1 Mahalanobis distances
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   2.2.2 μ-similar Bregman divergences
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   2.2.3 Examples of μ-similar Bregman divergences
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   3 The k-median problem
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  3.1 The generalized k-median problem
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   3.1.1 Definitions and notation
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   3.1.2 Properties
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   3.2 Bregman k-median clustering
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   3.3 A simple optimal algorithm for d=1
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   4 (1+ε)-approximate clustering by uniform sampling
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  4.1 Algorithm for [γ,δ]-sampleable dissimilarity measures
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   4.1.1 Superset sampling
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   4.1.2 The algorithm
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   4.1.3 Analysis for k=2
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   4.1.4 Analysis for k≥2
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   4.1.5 Adaptation for weighted input sets
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  4.2 Sampling for large classes of dissimilarity measures
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   4.2.1 Sampling for Mahalanobis distances
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   4.2.2 Sampling for μ-similar Bregman divergences
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   4.2.3 Sampling for the Hellinger distance
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   4.2.4 Sampling for arbitrary metrics with bounded doubling dimension
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   4.2.5 Sampling for the Hamming distance
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   4.3 Generalization of the sampling property
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   4.4 Discussion
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   5 A practical O(log k)-approximate algorithm
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  5.1 Algorithm BregMeans++
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   5.1.1 Non-uniform sampling scheme for -similar Bregman divergences
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   5.1.2 Proof of Theorem 5.3
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  5.2 Non-uniform sampling on separable instances
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   5.2.1 Separable input sets
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   5.2.2 Proof of Theorem 5.10
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   5.3 Discussion
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   6 Strong coresets for Mahalanobis distances
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   6.1 Har-Peled-Mazumdar coresets
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   6.1.1 Euclidean k-means clustering
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   6.1.2 Mahalanobis k-median clustering
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   6.1.3 Properties of Har-Peled-Mazumdar coresets
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   6.2 A new coreset construction based on non-uniform sampling
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   6.2.1 Coreset Construction
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   6.2.2 Proof of Theorem 6.14
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   6.3 Discussion
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   7 Weak coresets for μ-similar Bregman divergences
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  7.1 Construction of -weak coresets
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   7.1.1 Chen's coreset construction for Bregman divergences
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   7.1.2 Proof of Theorem 7.4
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   7.2 Application to Bregman k-median clustering
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   7.3 Discussion
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   8 On the limits of using uniform sampling
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   8.1 Sampleable Bregman divergences avoid singularities
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   8.2 Explicit domain bounds for some Bregman divergences
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   8.3 Discussion
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   A Applications
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   A.1 Estimating mixtures of identical Gaussian sources
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   A.2 Model reduction for data compression
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   A.3 Codebook generation for vector quantization in speech processing
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  B Mathematical fundamentals
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  B.1 The vectorspace ℝd
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   B.1.1 Vector arithmetic and inner product
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   B.1.2 Distances and metrics
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   B.1.3 Convex sets
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   B.1.4 Relative interior
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  B.2 Inequalities
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   B.2.1 Triangle inequality of the reals
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   B.2.2 Bounds to the binomial coefficient
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   B.2.3 Bounds on the harmonic number
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   B.2.4 Chebyshev's sum inequality
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  B.3 Calculus
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   B.3.1 Partial derivatives
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   B.3.2 Mean value theorem
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   B.3.3 Taylor expansion
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   B.3.4 Convex functions
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   B.3.5 Positive definiteness
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  B.4 Probability theory
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   B.4.1 Probability, expectation, and variance
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   B.4.2 Law of conditional probability
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   B.4.3 Law of total probability
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   B.4.4 Law of total expectation
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   B.4.5 Union bound
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   B.4.6 Markov's inequality
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   B.4.7 Chebyshev's inequality
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   B.4.8 Chernoff bounds