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84,734
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 59 (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
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
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 1614 (34 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
Text Classification from Labeled and Unlabeled Documents using EM
 MACHINE LEARNING
, 1999
"... This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large qua ..."
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Cited by 1033 (19 self)
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This paper shows that the accuracy of learned text classifiers can be improved by augmenting a small number of labeled training documents with a large pool of unlabeled documents. This is important because in many text classification problems obtaining training labels is expensive, while large
Bayesian matching of unlabelled point sets using Procrustes and configuration models
, 2010
"... ar ..."
Learning with local and global consistency
 Advances in Neural Information Processing Systems 16
, 2004
"... 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 intrinsic stru ..."
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Cited by 666 (21 self)
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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. 1
A framework for learning predictive structures from multiple tasks and unlabeled data
 Journal of Machine Learning Research
, 2005
"... One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semisupervised learning. Although a number of such methods ar ..."
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Cited by 440 (3 self)
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One of the most important issues in machine learning is whether one can improve the performance of a supervised learning algorithm by including unlabeled data. Methods that use both labeled and unlabeled data are generally referred to as semisupervised learning. Although a number of such methods
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 66 (12 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
Results 1  10
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