Results 1  10
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58
Learning Distance Functions Using Equivalence Relations
 In Proceedings of the Twentieth International Conference on Machine Learning
, 2003
"... We address the problem of learning distance metrics using sideinformation in the form of groups of "similar" points. We propose to use the RCA algorithm, which is a simple and e#cient algorithm for learning a full ranked Mahalanobis metric (Shental et al., 2002). ..."
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Cited by 137 (5 self)
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We address the problem of learning distance metrics using sideinformation in the form of groups of "similar" points. We propose to use the RCA algorithm, which is a simple and e#cient algorithm for learning a full ranked Mahalanobis metric (Shental et al., 2002).
Semisupervised learning with penalized probabilistic clustering
 In Advances in
, 2005
"... While clustering is usually an unsupervised operation, there are circumstances in which we believe (with varying degrees of certainty) that items A and B should be assigned to the same cluster, while items A and C should not. We would like such pairwise relations to influence cluster assignments of ..."
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Cited by 39 (1 self)
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While clustering is usually an unsupervised operation, there are circumstances in which we believe (with varying degrees of certainty) that items A and B should be assigned to the same cluster, while items A and C should not. We would like such pairwise relations to influence cluster assignments of outofsample data in a manner consistent with the prior knowledge expressed in the training set. Our starting point is probabilistic clustering based on Gaussian mixture models (GMM) of the data distribution. We express clustering preferences in the prior distribution over assignments of data points to clusters. This prior penalizes cluster assignments according to the degree with which they violate the preferences. We fit the model parameters with EM. Experiments on a variety of data sets show that PPC can consistently improve clustering results. 1
Hierarchical clustering of a mixture model
 In NIPS
, 2005
"... In this paper we propose an efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original model; this is achieved by clustering (grouping) the components. The method minimizes a new, easily computed distance measur ..."
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Cited by 35 (2 self)
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In this paper we propose an efficient algorithm for reducing a large mixture of Gaussians into a smaller mixture while still preserving the component structure of the original model; this is achieved by clustering (grouping) the components. The method minimizes a new, easily computed distance measure between two Gaussian mixtures that can be motivated from a suitable stochastic model and the iterations of the algorithm use only the model parameters, avoiding the need for explicit resampling of datapoints. We demonstrate the method by performing hierarchical clustering of scenery images and handwritten digits. 1
A Discriminative Learning Framework with Pairwise Constraints for Video Object Classification
 In Proc. of CVPR
, 2004
"... In video object classification, insufficient labeled data may at times be easily augmented with pairwise constraints on sample points, i.e, whether they are in the same class or not. In this paper, we proposed a discriminative learning approach which incorporates pairwise constraints into a conventi ..."
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Cited by 27 (5 self)
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In video object classification, insufficient labeled data may at times be easily augmented with pairwise constraints on sample points, i.e, whether they are in the same class or not. In this paper, we proposed a discriminative learning approach which incorporates pairwise constraints into a conventional marginbased learning framework. The proposed approach offers several advantages over existing approaches dealing with pairwise constraints. First, as opposed to learning distance metrics, the new approach derives its classification power by directly modeling the decision boundary. Second, most previous work handles labeled data by converting them to pairwise constraints and thus leads to much more computation. The proposed approach can handle pairwise constraints together with labeled data so that the computation is greatly reduced. The proposed approach is evaluated on a people classification task with two surveillance video datasets.
Learning with constrained and unlabeled data
 In CVPR
, 2005
"... Classification problems abundantly arise in many computer vision tasks – being of supervised, semisupervised or unsupervised nature. Even when class labels are not available, a user still might favor certain grouping solutions over others. This bias can be expressed either by providing a clustering ..."
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Cited by 24 (3 self)
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Classification problems abundantly arise in many computer vision tasks – being of supervised, semisupervised or unsupervised nature. Even when class labels are not available, a user still might favor certain grouping solutions over others. This bias can be expressed either by providing a clustering criterion or cost function and, in addition to that, by specifying pairwise constraints on the assignment of objects to classes. In this work, we discuss a unifying formulation for labelled and unlabelled data that can incorporate constrained data for model fitting. Our approach models the constraint information by the maximum entropy principle. This modeling strategy allows us (i) to handle constraint violations and soft constraints, and, at the same time, (ii) to speed up the optimization process. Experimental results on face classification and image segmentation indicates that the proposed algorithm is computationally efficient and generates superior groupings when compared with alternative techniques. 1.
Fast SDP relaxations of graph cut clustering, transduction, and other combinatorial problems
 JMLR
, 2006
"... The rise of convex programming has changed the face of many research fields in recent years, machine learning being one of the ones that benefitted the most. A very recent developement, the relaxation of combinatorial problems to semidefinite programs (SDP), has gained considerable attention over t ..."
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Cited by 21 (3 self)
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The rise of convex programming has changed the face of many research fields in recent years, machine learning being one of the ones that benefitted the most. A very recent developement, the relaxation of combinatorial problems to semidefinite programs (SDP), has gained considerable attention over the last decade (Helmberg, 2000; De Bie and Cristianini, 2004a). Although SDP problems can be solved in polynomial time, for many relaxations the exponent in the polynomial complexity bounds is too high for scaling to large problem sizes. This has hampered their uptake as a powerful new tool in machine learning. In this paper, we present a new and fast SDP relaxation of the normalized graph cut problem, and investigate its usefulness in unsupervised and semisupervised learning. In particular, this provides a convex algorithm for transduction, as well as approaches to clustering. We further propose a whole cascade of fast relaxations that all hold the middle between older spectral relaxations and the new SDP relaxation, allowing one to trade off computational cost versus relaxation accuracy. Finally, we discuss how the methodology developed in this paper can be applied to other combinatorial problems in machine learning, and we treat the maxcut problem as an example.
Semisupervised dimensionality reduction
 In: Proceedings of the 7th SIAM International Conference on Data Mining
, 2007
"... Dimensionality reduction is among the keys in mining highdimensional data. This paper studies semisupervised dimensionality reduction. In this setting, besides abundant unlabeled examples, domain knowledge in the form of pairwise constraints are available, which specifies whether a pair of instance ..."
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Cited by 20 (3 self)
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Dimensionality reduction is among the keys in mining highdimensional data. This paper studies semisupervised dimensionality reduction. In this setting, besides abundant unlabeled examples, domain knowledge in the form of pairwise constraints are available, which specifies whether a pair of instances belong to the same class (mustlink constraints) or different classes (cannotlink constraints). We propose the SSDR algorithm, which can preserve the intrinsic structure of the unlabeled data as well as both the mustlink and cannotlink constraints defined on the labeled examples in the projected lowdimensional space. The SSDR algorithm is efficient and has a closed form solution. Experiments on a broad range of data sets show that SSDR is superior to many established dimensionality reduction methods. 1
Learning to locate informative features for visual identification
 International Journal of Computer Vision
, 2005
"... Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, interobject variation is often small (many cars look alik ..."
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Cited by 18 (1 self)
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Object identification is a specialized type of recognition in which the category (e.g. cars) is known and the goal is to recognize an object’s exact identity (e.g. Bob’s BMW). Two special challenges characterize object identification. First, interobject variation is often small (many cars look alike) and may be dwarfed by illumination or pose changes. Second, there may be many different instances of the category but few or just one positive “training ” examples per object instance. Because variation among object instances may be small, a solution must locate possibly subtle objectspecific salient features, like a door handle, while avoiding distracting ones such as specular highlights. With just one training example per object instance, however, standard modeling and feature selection techniques cannot be used. We describe an online algorithm that takes one image from a known category and builds an efficient “same ” versus “different ” classification cascade by predicting the most discriminative features for that object instance. Our method not only estimates the saliency and scoring function for each candidate feature, but also models the dependency between features, building an ordered sequence of discriminative features specific to the given image. Learned stopping thresholds make the identifier very efficient. To make this possible, categoryspecific characteristics are learned automatically in an offline training procedure from labeled image pairs of the category. Our method, using the same algorithm for both cars and faces, outperforms a wide variety of other methods. 1.
BoostCluster: Boosting Clustering by Pairwise Constraints
"... Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints. However, these studies focus on designing special clustering algorithms that can effectively exploit t ..."
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Cited by 15 (6 self)
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Data clustering is an important task in many disciplines. A large number of studies have attempted to improve clustering by using the side information that is often encoded as pairwise constraints. However, these studies focus on designing special clustering algorithms that can effectively exploit the pairwise constraints. We present a boosting framework for data clustering, termed as BoostCluster, that is able to iteratively improve the accuracy of any given clustering algorithm by exploiting the pairwise constraints. The key challenge in designing a boosting framework for data clustering is how to influence an arbitrary clustering algorithm with the side information since clustering algorithms by definition are unsupervised. The proposed framework addresses this problem by dynamically generating new data representations at each iteration that are, on the one hand, adapted to the clustering results at previous iterations by the given algorithm, and on the other hand consistent with the given side information. Our empirical study shows that the proposed boosting framework is effective in improving the performance of a number of popular clustering algorithms (Kmeans, partitional SingleLink, spectral clustering), and its performance is comparable to the stateoftheart algorithms for data clustering with side information.
On Finding Melodic Lines in Audio Recordings
, 2004
"... The paper presents our approach to the problem of finding melodic line(s) in polyphonic audio recordings. The approach is composed of two different stages, partially rooted in psychoacoustic theories of music perception: the first stage is dedicated to finding regions with strong and stable pitch (m ..."
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Cited by 13 (1 self)
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The paper presents our approach to the problem of finding melodic line(s) in polyphonic audio recordings. The approach is composed of two different stages, partially rooted in psychoacoustic theories of music perception: the first stage is dedicated to finding regions with strong and stable pitch (melodic fragments), while in the second stage, these fragments are grouped according to their properties (pitch, loudness...) into clusters which represent melodic lines of the piece. Expectation Maximization algorithm is used in both stages to find the dominant pitch in a region, and to train Gaussian Mixture Models that group fragments into melodies. The paper presents the entire process in more detail and provides some initial results.