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101,115
Adaptive noisy clustering
, 2013
"... The problem of adaptive noisy clustering is investigated. Given a set of noisy observations Zi = Xi + ǫi, i = 1,..., n, the goal is to design clusters associated with the law of Xi’s, with unknown density f with respect to the Lebesgue measure. Since we observe a corrupted sample, a direct approach ..."
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Cited by 4 (1 self)
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The problem of adaptive noisy clustering is investigated. Given a set of noisy observations Zi = Xi + ǫi, i = 1,..., n, the goal is to design clusters associated with the law of Xi’s, with unknown density f with respect to the Lebesgue measure. Since we observe a corrupted sample, a direct approach
Adaptive Noisy Clustering
, 2014
"... The problem of adaptive noisy clustering is investigated. Given a set of noisy observations Zi = Xi + i, i = 1,..., n, the goal is to design clusters associated with the law of Xi’s, with unknown density f with respect to the Lebesgue measure. Since we observe a corrupted sample, a direct approach a ..."
Abstract
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The problem of adaptive noisy clustering is investigated. Given a set of noisy observations Zi = Xi + i, i = 1,..., n, the goal is to design clusters associated with the law of Xi’s, with unknown density f with respect to the Lebesgue measure. Since we observe a corrupted sample, a direct approach
Fast rates for Noisy Clustering Fast rates for Noisy Clustering
, 2012
"... The effect of errors in variables in empirical minimization is investigated. Given a loss l and a set of decision rules G, we prove a general upper bound for an empirical minimization based on a deconvolution kernel and a noisy sample Zi = Xi +ǫi,i = 1,...,n. We apply this general upper bound to giv ..."
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to give the rate of convergence for the expected excess risk in noisy clustering. A recent bound from Levrard (2012) proves that this rate is O(1/n) in the direct case, under Pollard’s regularity assumptions. Here the effect of noisy measurements gives a rate of the form O(1/n γ γ+2β), where γ
ModelBased Clustering, Discriminant Analysis, and Density Estimation
 JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
, 2000
"... Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However, there is little ..."
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Cited by 557 (28 self)
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Cluster analysis is the automated search for groups of related observations in a data set. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures and most clustering methods available in commercial software are also of this type. However
OPTICS: Ordering Points To Identify the Clustering Structure
, 1999
"... Cluster analysis is a primary method for database mining. It is either used as a standalone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of ..."
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Cited by 511 (49 self)
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Cluster analysis is a primary method for database mining. It is either used as a standalone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all
Projecting Dialect Distances to Geography: Bootstrap Clustering vs. Noisy Clustering
"... Abstract. Dialectometry produces aggregate distance matrices in which a distance is specified for each pair of sites. By projecting groups obtained by clustering onto geography one compares results with traditional dialectology, which produced maps partitioned into implicitly nonoverlapping dialect ..."
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Cited by 4 (3 self)
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Abstract. Dialectometry produces aggregate distance matrices in which a distance is specified for each pair of sites. By projecting groups obtained by clustering onto geography one compares results with traditional dialectology, which produced maps partitioned into implicitly non
Clustering Gene Expression Patterns
, 1999
"... Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the ana ..."
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Cited by 446 (11 self)
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in the analysis of gene expression data is the detection of groups of genes that manifest similar expression patterns. The corresponding algorithmic problem is to cluster multicondition gene expression patterns. In this paper we describe a novel clustering algorithm that was developed for analysis of gene
Robust Distributed Network Localization with Noisy Range Measurements
, 2004
"... This paper describes a distributed, lineartime algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities that otherw ..."
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Cited by 392 (21 self)
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This paper describes a distributed, lineartime algorithm for localizing sensor network nodes in the presence of range measurement noise and demonstrates the algorithm on a physical network. We introduce the probabilistic notion of robust quadrilaterals as a way to avoid flip ambiguities that otherwise corrupt localization computations. We formulate the localization problem as a twodimensional graph realization problem: given a planar graph with approximately known edge lengths, recover the Euclidean position of each vertex up to a global rotation and translation. This formulation is applicable to the localization of sensor networks in which each node can estimate the distance to each of its neighbors, but no absolute position reference such as GPS or fixed anchor nodes is available. We implemented the algorithm on a physical sensor network and empirically assessed its accuracy and performance. Also, in simulation, we demonstrate that the algorithm scales to large networks and handles realworld deployment geometries. Finally, we show how the algorithm supports localization of mobile nodes.
How many clusters? Which clustering method? Answers via modelbased cluster analysis
 THE COMPUTER JOURNAL
, 1998
"... ..."
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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, assessing predictions, handling noisy data and outliers, improving the quality of predictions by tuning t parameters, interference between old and new data, implementing locally weighted learning e ciently, and applications of locally weighted learning. A companion paper surveys how locally weighted
Results 1  10
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