Quantization
RM Gray, DL Neuhoff - IEEE transactions on information theory, 1998 - ieeexplore.ieee.org
The history of the theory and practice of quantization dates to 1948, although similar ideas
had appeared in the literature as long ago as 1898. The fundamental role of quantization in …
had appeared in the literature as long ago as 1898. The fundamental role of quantization in …
[图书][B] Learning with kernels
AJ Smola, B Schölkopf - 1998 - Citeseer
Abstract Support Vector (SV) Machines combine several techniques from statistics, machine
learning and neural networks. One of the most important ingredients are kernels, ie the …
learning and neural networks. One of the most important ingredients are kernels, ie the …
Consistency of spectral clustering
Consistency is a key property of all statistical procedures analyzing randomly sampled data.
Surprisingly, despite decades of work, little is known about consistency of most clustering …
Surprisingly, despite decades of work, little is known about consistency of most clustering …
Spectral ensemble clustering via weighted k-means: Theoretical and practical evidence
As a promising way for heterogeneous data analytics, consensus clustering has attracted
increasing attention in recent decades. Among various excellent solutions, the co …
increasing attention in recent decades. Among various excellent solutions, the co …
Sample complexity of testing the manifold hypothesis
H Narayanan, S Mitter - Advances in neural information …, 2010 - proceedings.neurips.cc
The hypothesis that high dimensional data tends to lie in the vicinity of a low dimensional
manifold is the basis of a collection of methodologies termed Manifold Learning. In this …
manifold is the basis of a collection of methodologies termed Manifold Learning. In this …
Spectral ensemble clustering
Ensemble clustering, also known as consensus clustering, is emerging as a promising
solution for multi-source and/or heterogeneous data clustering. The co-association matrix …
solution for multi-source and/or heterogeneous data clustering. The co-association matrix …
Empirical risk minimization for heavy-tailed losses
The purpose of this paper is to discuss empirical risk minimization when the losses are not
necessarily bounded and may have a distribution with heavy tails. In such situations, usual …
necessarily bounded and may have a distribution with heavy tails. In such situations, usual …
Statistical inference under multiterminal data compression
S Amari - IEEE Transactions on Information Theory, 1998 - ieeexplore.ieee.org
This paper presents a survey of the literature on the information-theoretic problems of
statistical inference under multiterminal data compression with rate constraints. Significant …
statistical inference under multiterminal data compression with rate constraints. Significant …
On the performance of clustering in Hilbert spaces
Based on randomly drawn vectors in a separable Hilbert space, one may construct a k-
means clustering scheme by minimizing an empirical squared error. We investigate the risk …
means clustering scheme by minimizing an empirical squared error. We investigate the risk …
Sharper generalization bounds for clustering
S Li, Y Liu - International Conference on Machine Learning, 2021 - proceedings.mlr.press
Existing generalization analysis of clustering mainly focuses on specific instantiations, such
as (kernel) $ k $-means, and a unified framework for studying clustering performance is still …
as (kernel) $ k $-means, and a unified framework for studying clustering performance is still …