Results 1  10
of
39
Distance metric learning for large margin nearest neighbor classification
 In NIPS
, 2006
"... We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven ..."
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Cited by 326 (10 self)
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We show how to learn a Mahanalobis distance metric for knearest neighbor (kNN) classification by semidefinite programming. The metric is trained with the goal that the knearest neighbors always belong to the same class while examples from different classes are separated by a large margin. On seven data sets of varying size and difficulty, we find that metrics trained in this way lead to significant improvements in kNN classification—for example, achieving a test error rate of 1.3 % on the MNIST handwritten digits. As in support vector machines (SVMs), the learning problem reduces to a convex optimization based on the hinge loss. Unlike learning in SVMs, however, our framework requires no modification or extension for problems in multiway (as opposed to binary) classification. 1
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 29 (2 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
Supervised Semantic Indexing
"... Abstract. We present a class of models that are discriminatively trained to directly map from the word content in a querydocument or documentdocument pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, un ..."
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Cited by 17 (6 self)
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Abstract. We present a class of models that are discriminatively trained to directly map from the word content in a querydocument or documentdocument pair to a ranking score. Like Latent Semantic Indexing (LSI), our models take account of correlations between words (synonymy, polysemy). However, unlike LSI our models are trained with a supervised signal directly on the task of interest, which we argue is the reason for our superior results. We provide an empirical study on Wikipedia documents, using the links to define documentdocument or querydocument pairs, where we obtain stateoftheart performance using our method. Key words: supervised, semantic indexing, document ranking 1
Sparse Metric Learning via Smooth Optimization
"... In this paper we study the problem of learning a lowrank (sparse) distance matrix. We propose a novel metric learning model which can simultaneously conduct dimension reduction and learn a distance matrix. The sparse representation involves a mixednorm regularization which is nonconvex. We then s ..."
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Cited by 14 (1 self)
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In this paper we study the problem of learning a lowrank (sparse) distance matrix. We propose a novel metric learning model which can simultaneously conduct dimension reduction and learn a distance matrix. The sparse representation involves a mixednorm regularization which is nonconvex. We then show that it can be equivalently formulated as a convex saddle (minmax) problem. From this saddle representation, we develop an efficient smooth optimization approach [17] for sparse metric learning, although the learning model is based on a nondifferentiable loss function. Finally, we run experiments to validate the effectiveness and efficiency of our sparse metric learning model on various datasets. 1
Multiobject tracking as maximum weight independent set
 In Proc. IEEE Conf. on Computer Vision and Pattern Recognition
, 2011
"... This paper addresses the problem of simultaneous tracking of multiple targets in a video. We first apply object detectors to every video frame. Pairs of detection responses from every two consecutive frames are then used to build a graph of tracklets. The graph helps transitively link the best match ..."
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Cited by 12 (1 self)
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This paper addresses the problem of simultaneous tracking of multiple targets in a video. We first apply object detectors to every video frame. Pairs of detection responses from every two consecutive frames are then used to build a graph of tracklets. The graph helps transitively link the best matching tracklets that do not violate hard and soft contextual constraints between the resulting tracks. We prove that this data association problem can be formulated as finding the maximumweight independent set (MWIS) of the graph. We present a new, polynomialtime MWIS algorithm, and prove that it converges to an optimum. Similarity and contextual constraints between object detections, used for data association, are learned online from object appearance and motion properties. Longterm occlusions are addressed by iteratively repeating MWIS to hierarchically merge smaller tracks into longer ones. Our results demonstrate advantages of simultaneously accounting for soft and hard contextual constraints in multitarget tracking. We outperform the state of the art on the benchmark datasets. 1.
An Online Algorithm for Large Scale Image Similarity Learning
"... Learning a measure of similarity between pairs of objects is a fundamental problem in machine learning. It stands in the core of classification methods like kernel machines, and is particularly useful for applications like searching for images that are similar to a given image or finding videos that ..."
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Cited by 11 (0 self)
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Learning a measure of similarity between pairs of objects is a fundamental problem in machine learning. It stands in the core of classification methods like kernel machines, and is particularly useful for applications like searching for images that are 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, current approaches for learning similarity do not scale to large datasets, especially when imposing metric constraints on the learned similarity. We describe OASIS, a method for learning pairwise similarity that is fast and scales linearly with the number of objects and the number of nonzero features. Scalability is achieved through online learning of a bilinear model over sparse representations using a large margin criterion and an efficient hinge loss cost. OASIS is accurate at a wide range of scales: on a standard benchmark with thousands of images, it is more precise than stateoftheart methods, and faster by orders of magnitude. On 2.7 million images collected from the web, OASIS can be trained within 3 days on a single CPU. The nonmetric similarities learned by OASIS can be transformed into metric similarities, achieving higher precisions than similarities that are learned as metrics in the first place. This suggests an approach for learning a metric from data that is larger by orders of magnitude than was handled before. 1
Metric and Kernel Learning Using a Linear Transformation
"... Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over lowdimensional data, while existing kernel learning algorithms are often limited to the transductive setting and do not generalize to new ..."
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Cited by 10 (2 self)
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Metric and kernel learning arise in several machine learning applications. However, most existing metric learning algorithms are limited to learning metrics over lowdimensional data, while existing kernel learning algorithms are often limited to the transductive setting and do not generalize to new data points. In this paper, we study the connections between metric learning and kernel learning that arise when studying metric learning as a linear transformation learning problem. In particular, we propose a general optimization framework for learning metrics via linear transformations, and analyze in detail a special case of our framework—that of minimizing the LogDet divergence subject to linear constraints. We then propose a general regularized framework for learning a kernel matrix, and show it to be equivalent to our metric learning framework. Our theoretical connections between metric and kernel learning have two main consequences: 1) the learned kernel matrix parameterizes a linear transformation kernel function and can be applied inductively to new data points, 2) our result yields a constructive method for kernelizing most existing Mahalanobis metric learning formulations. We demonstrate our learning approach by applying it to largescale real world problems in computer vision, text mining and semisupervised kernel dimensionality reduction. Keywords: divergence metric learning, kernel learning, linear transformation, matrix divergences, logdet 1.
Polynomial Semantic Indexing
"... We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a querydocument or documentdocument pair to a ranking score. Dealing with polynomial models on word features is computationally challenging. We propose a lowrank (but diag ..."
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Cited by 9 (4 self)
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We present a class of nonlinear (polynomial) models that are discriminatively trained to directly map from the word content in a querydocument or documentdocument pair to a ranking score. Dealing with polynomial models on word features is computationally challenging. We propose a lowrank (but diagonal preserving) representation of our polynomial models to induce feasible memory and computation requirements. We provide an empirical study on retrieval tasks based on Wikipedia documents, where we obtain stateoftheart performance while providing realistically scalable methods. 1
Distance metric learning with eigenvalue optimization
 Journal of Machine Learning Research (Special Topics on Kernel and Metric Learning
, 2012
"... The main theme of this paper is to develop a novel eigenvalue optimization framework for learning a Mahalanobis metric. Within this context, we introduce a novel metric learning approach called DMLeig which is shown to be equivalent to a wellknown eigenvalue optimization problem called minimizing ..."
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Cited by 7 (0 self)
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The main theme of this paper is to develop a novel eigenvalue optimization framework for learning a Mahalanobis metric. Within this context, we introduce a novel metric learning approach called DMLeig which is shown to be equivalent to a wellknown eigenvalue optimization problem called minimizing the maximal eigenvalue of a symmetric matrix (Overton, 1988; Lewis and Overton, 1996). Moreover, we formulate LMNN (Weinberger et al., 2005), one of the stateoftheart metric learning methods, as a similar eigenvalue optimization problem. This novel framework not only provides new insights into metric learning but also opens new avenues to the design of efficient metric learning algorithms. Indeed, firstorder algorithms are developed for DMLeig and LMNN which only need the computation of the largest eigenvector of a matrix per iteration. Their convergence characteristics are rigorously established. Various experiments on benchmark data sets show the competitive performance of our new approaches. In addition, we report an encouraging result on a difficult and challenging face verification data set called Labeled Faces in the Wild (LFW).
Relaxed pairwise learned metric for person reidentification
 In ECCV
, 2012
"... Abstract. Matching persons across nonoverlapping cameras is a rather challenging task. Thus, successful methods often build on complex feature representations or sophisticated learners. A recent trend to tackle this problem is to use metric learning to find a suitable space for matching samples fro ..."
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Cited by 6 (1 self)
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Abstract. Matching persons across nonoverlapping cameras is a rather challenging task. Thus, successful methods often build on complex feature representations or sophisticated learners. A recent trend to tackle this problem is to use metric learning to find a suitable space for matching samples from different cameras. However, most of these approaches ignore the transition from one camera to the other. In this paper, we propose to learn a metric from pairs of samples from different cameras. In this way, even less sophisticated features describing color and texture information are sufficient for finally getting stateoftheart classification results. Moreover, once the metric has been learned, only linear projections are necessary at search time, where a simple nearest neighbor classification is performed. The approach is demonstrated on three publicly available datasets of different complexity, where it can be seen that stateoftheart results can be obtained at much lower computational costs. 1