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173
Neighbourhood components analysis
 Advances in Neural Information Processing Systems 17
, 2004
"... In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leaveoneout KNN score on the training set. It can also learn a lowdimensional linear embedding of labele ..."
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Cited by 338 (9 self)
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In this paper we propose a novel method for learning a Mahalanobis distance measure to be used in the KNN classification algorithm. The algorithm directly maximizes a stochastic variant of the leaveoneout KNN score on the training set. It can also learn a lowdimensional linear embedding of labeled data that can be used for data visualization and fast classification. Unlike other methods, our classification model is nonparametric, making no assumptions about the shape of the class distributions or the boundaries between them. The performance of the method is demonstrated on several data sets, both for metric learning and linear dimensionality reduction. 1
Data Clustering: 50 Years Beyond KMeans
, 2008
"... Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and m ..."
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Cited by 274 (6 self)
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Organizing data into sensible groupings is one of the most fundamental modes of understanding and learning. As an example, a common scheme of scientific classification puts organisms into taxonomic ranks: domain, kingdom, phylum, class, etc.). Cluster analysis is the formal study of algorithms and methods for grouping, or clustering, objects according to measured or perceived intrinsic characteristics or similarity. Cluster analysis does not use category labels that tag objects with prior identifiers, i.e., class labels. The absence of category information distinguishes data clustering (unsupervised learning) from classification or discriminant analysis (supervised learning). The aim of clustering is exploratory in nature to find structure in data. Clustering has a long and rich history in a variety of scientific fields. One of the most popular and simple clustering algorithms, Kmeans, was first published in 1955. In spite of the fact that Kmeans was proposed over 50 years ago and thousands of clustering algorithms have been published since then, Kmeans is still widely used. This speaks to the difficulty of designing a general purpose clustering algorithm and the illposed problem of clustering. We provide a brief overview of clustering, summarize well known clustering methods, discuss the major challenges and key issues in designing clustering algorithms, and point out some of the emerging and useful research directions, including semisupervised clustering, ensemble clustering, simultaneous feature selection, and data clustering and large scale data clustering.
A Probabilistic Framework for SemiSupervised Clustering
, 2004
"... Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supe ..."
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Cited by 271 (14 self)
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Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supervision. Such methods use the constraints to either modify the objective function, or to learn the distance measure. We propose a probabilistic model for semisupervised clustering based on Hidden Markov Random Fields (HMRFs) that provides a principled framework for incorporating supervision into prototypebased clustering. The model generalizes a previous approach that combines constraints and Euclidean distance learning, and allows the use of a broad range of clustering distortion measures, including Bregman divergences (e.g., Euclidean distance and Idivergence) and directional similarity measures (e.g., cosine similarity). We present an algorithm that performs partitional semisupervised clustering of data by minimizing an objective function derived from the posterior energy of the HMRF model. Experimental results on several text data sets demonstrate the advantages of the proposed framework. 1.
Semisupervised graph clustering: a kernel approach
, 2008
"... Semisupervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semisupervised clustering algorithms are designed for data represented as vectors. In this ..."
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Cited by 94 (3 self)
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Semisupervised clustering algorithms aim to improve clustering results using limited supervision. The supervision is generally given as pairwise constraints; such constraints are natural for graphs, yet most semisupervised clustering algorithms are designed for data represented as vectors. In this paper, we unify vectorbased and graphbased approaches. We first show that a recentlyproposed objective function for semisupervised clustering based on Hidden Markov Random Fields, with squared Euclidean distance and a certain class of constraint penalty functions, can be expressed as a special case of the weighted kernel kmeans objective (Dhillon et al., in Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, 2004a). A recent theoretical connection between weighted kernel kmeans and several graph clustering objectives enables us to perform semisupervised clustering of data given either as vectors or as a graph. For graph data, this result leads to algorithms for optimizing several new semisupervised graph clustering objectives. For vector data, the kernel approach also enables us to find clusters with nonlinear boundaries in the input data space. Furthermore, we show that recent work on spectral learning (Kamvar et al., in Proceedings of the 17th International Joint Conference on Artificial Intelligence, 2003) may be viewed as a special case of our formulation. We empirically show that our algorithm is able to outperform current stateoftheart semisupervised algorithms on both vectorbased and graphbased data sets.
Large Scale Online Learning of Image Similarity through Ranking
"... Learning a measure of similarity between pairs of objects is an important generic problem in machine learning. It is particularly useful in large scale applications like searching for an image that is similar to a given image or finding videos that are relevant to a given video. In these tasks, user ..."
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Cited by 79 (3 self)
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Learning a measure of similarity between pairs of objects is an important generic problem in machine learning. It is particularly useful in large scale applications like searching for an image that is similar to a given image or finding videos that are relevant to a given video. In these tasks, users look for objects that are not only visually similar but also semantically related to a given object. Unfortunately, the approaches that exist today for learning such semantic similarity do not scale to large datasets. This is both because typically their CPU and storage requirements grow quadratically with the sample size, and because many methods impose complex positivity constraints on the space of learned similarity functions. The current paper presents OASIS, an Online Algorithm for Scalable Image Similarity learning that learns a bilinear similarity measure over sparse representations. OASIS is an online dual approach using the passiveaggressive family of learning algorithms with a large margin criterion and an efficient hinge loss cost. Our experiments show that OASIS is both fast and accurate at a wide range of scales: for a dataset with thousands of images, it achieves better results than existing stateoftheart methods, while being an order of
Y.: Descriptor based methods in the wild
 In: Faces in RealLife Images Workshop in ECCV. (2008) (b) Similarity Scores based on Background Samples
"... Abstract. Recent methods for learning similarity between images have presented impressive results in the problem of pair matching (same/notsame classification) of face images. In this paper we explore how well this performance carries over to the related task of multioption face identification, spe ..."
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Cited by 69 (13 self)
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Abstract. Recent methods for learning similarity between images have presented impressive results in the problem of pair matching (same/notsame classification) of face images. In this paper we explore how well this performance carries over to the related task of multioption face identification, specifically on the Labeled Faces in the Wild (LFW) image set. In addition, we seek to compare the performance of similarity learning methods to descriptor based methods. We present the following results: (1) DescriptorBased approaches that efficiently encode the appearance of each face image as a vector outperform the leading similarity based method in the task of multioption face identification. (2) Straightforward use of Euclidean distance on the descriptor vectors performs somewhat worse than the similarity learning methods on the task of pair matching. (3) Adding a learning stage, the performance of descriptor based methods matches and exceeds that of similarity methods on the pair matching task. (4) A novel patch based descriptor we propose is able to improve the performance of the successful Local Binary Pattern (LBP) descriptor in both multioption identification and same/notsame classification. 1
Similarity Scores based on Background Samples
"... Abstract. Evaluating the similarity of images and their descriptors by employing discriminative learners has proven itself to be an effective face recognition paradigm. In this paper we show how “background samples”, that is, examples which do not belong to any of the classes being learned, may prov ..."
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Cited by 64 (6 self)
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Abstract. Evaluating the similarity of images and their descriptors by employing discriminative learners has proven itself to be an effective face recognition paradigm. In this paper we show how “background samples”, that is, examples which do not belong to any of the classes being learned, may provide a significant performance boost to such face recognition systems. In particular, we make the following contributions. First, we define and evaluate the “TwoShot Similarity ” (TSS) score as an extension to the recently proposed “OneShot Similarity ” (OSS) measure. Both these measures utilize background samples to facilitate better recognition rates. Second, we examine the ranking of images most similar to a query image and employ these as a descriptor for that image. Finally, we provide results underscoring the importance of proper face alignment in automatic face recognition systems. These contributions in concert allow us to obtain a success rate of 86.83 % on the Labeled Faces in the Wild (LFW) benchmark, outperforming current stateoftheart results. 1
Diffrac: a discriminative and flexible framework for clustering
 In Advances in Neural Information Processing Systems 20
, 2007
"... We present a novel linear clustering framework (DIFFRAC) which relies on a linear discriminative cost function and a convex relaxation of a combinatorial optimization problem. The large convex optimization problem is solved through a sequence of lower dimensional singular value decompositions. Thi ..."
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Cited by 54 (11 self)
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We present a novel linear clustering framework (DIFFRAC) which relies on a linear discriminative cost function and a convex relaxation of a combinatorial optimization problem. The large convex optimization problem is solved through a sequence of lower dimensional singular value decompositions. This framework has several attractive properties: (1) although apparently similar to Kmeans, it exhibits superior clustering performance than Kmeans, in particular in terms of robustness to noise. (2) It can be readily extended to non linear clustering if the discriminative cost function is based on positive definite kernels, and can then be seen as an alternative to spectral clustering. (3) Prior information on the partition is easily incorporated, leading to stateoftheart performance for semisupervised learning, for clustering or classification. We present empirical evaluations of our algorithms on synthetic and real mediumscale datasets. 1