Foundations of Quantization for Probability Distributions
Foundations of Quantization for Probability Distributions
Lectures on Discrete Geometry
Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Candid Covariance-Free Incremental Principal Component Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering Large Graphs via the Singular Value Decomposition
Machine Learning
A Simple Linear Time (1+ ") -Approximation Algorithm for k-Means Clustering in Any Dimensions
FOCS '04 Proceedings of the 45th Annual IEEE Symposium on Foundations of Computer Science
Finding the Homology of Submanifolds with High Confidence from Random Samples
Discrete & Computational Geometry
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Near-Optimal Signal Recovery From Random Projections: Universal Encoding Strategies?
IEEE Transactions on Information Theory
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A simple and computationally efficient scheme for tree-structured vector quantization is presented. Unlike previous methods, its quantization error depends only on the intrinsic dimension of the data distribution, rather than the apparent dimension of the space in which the data happen to lie.