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32,933
Unsupervised learning of an atlas from unlabeled pointsets
 IEEE Trans. Pattern Anal. Mach. Intell
, 2004
"... One of the key challenges in deformable shape modeling is the problem of estimating a meaningful average or mean shape from a set of unlabeled shapes. We present a new joint clustering and matching algorithm that is capable of computing such a mean shape from multiple shape samples which are represe ..."
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Cited by 60 (2 self)
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are represented by unlabeled pointsets. An iterative bootstrap process is used wherein multiple shape sample pointsets are nonrigidly deformed to the emerging mean shape, with subsequent estimation of the mean shape based on these nonrigid alignments. The process is entirely symmetric with no bias toward any
CLASSIFICATION OF UNLABELED POINT SETS USING ANSIG
"... We address twodimensional shapebased classification, considering shapes described by arbitrary sets of unlabeled points, or landmarks. This is relevant in practice because, in many applications, the points describing the shapes come from automatic processes, e.g., edge detection, thus without labe ..."
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Cited by 3 (2 self)
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We address twodimensional shapebased classification, considering shapes described by arbitrary sets of unlabeled points, or landmarks. This is relevant in practice because, in many applications, the points describing the shapes come from automatic processes, e.g., edge detection, thus without
Learning an Atlas From Unlabeled PointSets
 IEEE Trans. Pattern Anal. Mach. Intell
, 2001
"... One of the key challenges in deformable shape modeling is the problem of estimating a meaningful average or mean shape from a set of unlabeled shapes. We present a new joint clustering and matching algorithm that is capable of computing such a mean shape from multiple shape samples which are represe ..."
Abstract
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are represented by unlabeled pointsets. An iterative bootstrap process is used wherein multiple shape sample pointsets are nonrigidly deformed to the emerging mean shape, with subsequent estimation of the mean shape based on these nonrigid alignments. The process is entirely symmetric with no bias toward any
Combining labeled and unlabeled data with cotraining
, 1998
"... We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated by the ta ..."
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Cited by 1633 (28 self)
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We consider the problem of using a large unlabeled sample to boost performance of a learning algorithm when only a small set of labeled examples is available. In particular, we consider a setting in which the description of each example can be partitioned into two distinct views, motivated
Template estimation form unlabeled point set data and surfaces for computational anatomy
 In Mathematical Foundations of Computational Anatomy: Geometrical and Statistical Methods for Modelling Biological Shape Variability
, 2006
"... Abstract. A central notion in Computational Anatomy is the generation of registration maps,mapping a large set of anatomical data to a common coordinate system to study intrapopulation variability and interpopulation differences. In previous work [1, 2] methods for estimating the common coordina ..."
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Cited by 15 (3 self)
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coordinate system or the template given a collection imaging data were presented based on the notion of Fréchet mean estimation using a metric on the space of diffeomorphisms. In this paper we extend the methodology to the estimation of a template given a collection of unlabeled point sets and surfaces
Bayesian matching of unlabelled point sets using Procrustes and configuration models
, 2010
"... ar ..."
Learning with local and global consistency.
 In NIPS,
, 2003
"... Abstract We consider the general problem of learning from labeled and unlabeled data, which is often called semisupervised learning or transductive inference. A principled approach to semisupervised learning is to design a classifying function which is sufficiently smooth with respect to the intr ..."
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Cited by 673 (21 self)
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to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problems and demonstrates effective use of unlabeled data.
Diffeomorphic matching of distributions: A new approach for unlabelled pointsets and submanifolds matching
 In CVPR (pp. 712–718). Los Alamitos: IEEE Comput. Soc
, 2004
"... In the paper, we study the problem of optimal matching of two generalized functions (distributions) via a diffeomorphic transformation of the ambient space. In the particular case of discrete distributions (weighted sums of Dirac measures), we provide a new algorithm to compare two arbitrary unlabel ..."
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Cited by 63 (11 self)
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unlabelled sets of points, and show that it behaves properly in limit of continuous distributions on submanifolds. As a consequence, the algorithm may apply to various matching problems, such as curve or surface matching (via a subsampling), or mixings of landmark and curve data. As the solution forbids
Estimating the Support of a HighDimensional Distribution
, 1999
"... Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We propo ..."
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Cited by 783 (29 self)
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Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We
Results 1  10
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32,933