Results 1  10
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18
Nonparametric probabilistic image segmentation
 In ICCV
, 2007
"... All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately. ..."
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Cited by 11 (2 self)
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All intext references underlined in blue are linked to publications on ResearchGate, letting you access and read them immediately.
Using graph partitioning techniques for neighbour selection in userbased collaborative filtering
 In Proceedings of the sixth ACM conference on Recommender systems, RecSys ’12, ACM
, 2012
"... Spectral clustering techniques have become one of the most popular clustering algorithms, mainly because of their simplicity and effectiveness. In this work, we make use of one of these techniques, Normalised Cut, in order to derive a clusterbased collaborative filtering algorithm which outperforms ..."
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Cited by 7 (1 self)
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Spectral clustering techniques have become one of the most popular clustering algorithms, mainly because of their simplicity and effectiveness. In this work, we make use of one of these techniques, Normalised Cut, in order to derive a clusterbased collaborative filtering algorithm which outperforms other standard techniques in the stateoftheart in terms of ranking precision. We frame this technique as a method for neighbour selection, and we show its effectiveness when compared with other clusterbased methods. Furthermore, the performance of our method could be improved if standard similarity metrics – such as Pearson’s correlation – are also used when predicting the user’s preferences.
Unsupervised Learning of Categorical Segments in Image Collections
, 2011
"... Which one comes first: segmentation or recognition? We propose a unified framework for carrying out the two simultaneously and without supervision. The framework combines a flexible probabilistic model, for representing the shape and appearance of each segment, with the popular “bag of visual words ..."
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Cited by 4 (0 self)
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Which one comes first: segmentation or recognition? We propose a unified framework for carrying out the two simultaneously and without supervision. The framework combines a flexible probabilistic model, for representing the shape and appearance of each segment, with the popular “bag of visual words ” model for recognition. If applied to a collection of images, our framework can simultaneously discover the segments of each image, and the correspondence between such segments, without supervision. Such recurring segments may be thought of as the ‘parts ’ of corresponding objects that appear multiple times in the image collection. Thus, the model may be used for learning new categories, detecting/classifying objects, and segmenting images, without using expensive human annotation. Index Terms Computer Vision, image segmentation, unsupervised object recognition, graphical models, density estimation, scene analysis I.
Factorized Diffusion Map Approximation
"... Diffusion maps are among the most powerful Machine Learning tools to analyze and work with complex highdimensional datasets. Unfortunately, the estimation of these maps from a finite sample is known to suffer from the curse of dimensionality. Motivated by other machine learning models for which the ..."
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Cited by 3 (3 self)
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Diffusion maps are among the most powerful Machine Learning tools to analyze and work with complex highdimensional datasets. Unfortunately, the estimation of these maps from a finite sample is known to suffer from the curse of dimensionality. Motivated by other machine learning models for which the existence of structure in the underlying distribution of data can reduce the complexity of estimation, we study and show how the factorization of the underlying distribution into independent subspaces can help us to estimate diffusion maps more accurately. Building upon this result, we propose and develop an algorithm that can automatically factorize a high dimensional data space in order to minimize the error of estimation of its diffusion map, even in the case when the underlying distribution is not decomposable. Experiments on both the synthetic and realworld datasets demonstrate improved estimation performance of our method over the standard diffusionmap framework. 1
A Mixture Model for Spike Train Ensemble Analysis Using Spectral Clustering
 in Proc. of ICASSP
, 2006
"... Identifying clusters of neurons with correlated spiking activity in largesize neuronal ensembles recorded with highdensity multielectrode array is an emerging problem in computational neuroscience. We propose a nonparametric approach that represents multiple neural spike trains by a mixed point pro ..."
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Identifying clusters of neurons with correlated spiking activity in largesize neuronal ensembles recorded with highdensity multielectrode array is an emerging problem in computational neuroscience. We propose a nonparametric approach that represents multiple neural spike trains by a mixed point process model. A spectral clustering algorithm is applied to identify the clusters of neurons through their correlated firing activities. The advantage of the proposed technique is its ability to efficiently identify large populations of neurons with correlated spiking activity independent of the temporal scale. We report the clustering performance of the algorithm applied to a complex synthesized data set and compare it to multiple clustering techniques. 1.
Language Modelling of Constraints for Text Clustering
"... Abstract. Constrained clustering is a recently presented family of semisupervised learning algorithms. These methods use domain information to impose constraints over the clustering output. The way in which those constraints (typically pairwise constraints between documents) are introduced is by ..."
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Abstract. Constrained clustering is a recently presented family of semisupervised learning algorithms. These methods use domain information to impose constraints over the clustering output. The way in which those constraints (typically pairwise constraints between documents) are introduced is by designing new clustering algorithms that enforce the accomplishment of the constraints. In this paper we present an alternative approach for constrained clustering where, instead of defining new algorithms or objective functions, the constraints are introduced modifying the document representation by means of their language modelling. More precisely the constraints are modelled using the wellknown Relevance Models successfully used in other retrieval tasks such as pseudorelevance feedback. To the best of our knowledge this is the first attempt to try such approach. The results show that the presented approach is an effective method for constrained clustering even improving the results of existing constrained clustering algorithms. 1
SemiSupervised Collaborative Text Classification
"... Abstract. Most text categorization methods require text content of documents that is often difficult to obtain. We consider “Collaborative Text Categorization”, where each document is represented by the feedback from a large number of users. Our study focuses on the semisupervised case in which one ..."
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Abstract. Most text categorization methods require text content of documents that is often difficult to obtain. We consider “Collaborative Text Categorization”, where each document is represented by the feedback from a large number of users. Our study focuses on the semisupervised case in which one key challenge is that a significant number of users have not rated any labeled document. To address this problem, we examine several semisupervised learning methods and our empirical study shows that collaborative text categorization is more effective than contentbased text categorization and the manifold regularization is more effective than other stateoftheart semisupervised learning methods. 1
Abstract
"... Maximum margin clustering was proposed lately and has shown promising performance in recent studies [1, 2]. It extends the theory of support vector machine to unsupervised learning. Despite its good performance, there are three major problems with maximum margin clustering that question its efficien ..."
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Maximum margin clustering was proposed lately and has shown promising performance in recent studies [1, 2]. It extends the theory of support vector machine to unsupervised learning. Despite its good performance, there are three major problems with maximum margin clustering that question its efficiency for realworld applications. First, it is computationally expensive and difficult to scale to largescale datasets because the number of parameters in maximum margin clustering is quadratic in the number of examples. Second, it requires data preprocessing to ensure that any clustering boundary will pass through the origins, which makes it unsuitable for clustering unbalanced dataset. Third, it is sensitive to the choice of kernel functions, and requires external procedure to determine the appropriate values for the parameters of kernel functions. In this paper, we propose “generalized maximum margin clustering ” framework that addresses the above three problems simultaneously. The new framework generalizes the maximum margin clustering algorithm by allowing any clustering boundaries including those not passing through the origins. It significantly improves the computational efficiency by reducing the number of parameters. Furthermore, the new framework is able to automatically determine the appropriate kernel matrix without any labeled data. Finally, we show a formal connection between maximum margin clustering and spectral clustering. We demonstrate the efficiency of the generalized maximum margin clustering algorithm using both synthetic datasets and real datasets from the UCI repository. 1
pejusdas[at]cse[dot]buffalo[dot]edu
"... mbeal[at]cse[dot]buffalo[dot]edu Spectral techniques, off late, have been in limelight in the machine learning community and has drawn attention of many serious machine learners. They are being used in a variety of applications like gene clustering, document analysis, image segmentation, dimensional ..."
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mbeal[at]cse[dot]buffalo[dot]edu Spectral techniques, off late, have been in limelight in the machine learning community and has drawn attention of many serious machine learners. They are being used in a variety of applications like gene clustering, document analysis, image segmentation, dimensionality reduction etc. They are very simple to understand and provide highly accurate results even for difficult clustering problems. Due to their rise in popularity and their importance in every machine learners life we present some spectral clustering techniques available in the literature. We draw similarities between them and try to portray the central idea of spectral clustering. This paper can be thought of as a headway into this interesting and upcoming field. 1