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695
Large margin component analysis
 Advances in Neural Information Processing Systems 19
, 2006
"... Metric learning has been shown to significantly improve the accuracy of knearest neighbor (kNN) classification. In problems involving thousands of features, distance learning algorithms cannot be used due to overfitting and high computational complexity. In such cases, previous work has relied on ..."
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Cited by 56 (2 self)
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Metric learning has been shown to significantly improve the accuracy of knearest neighbor (kNN) classification. In problems involving thousands of features, distance learning algorithms cannot be used due to overfitting and high computational complexity. In such cases, previous work has relied on a twostep solution: first apply dimensionality reduction methods to the data, and then learn a metric in the resulting lowdimensional subspace. In this paper we show that better classification performance can be achieved by unifying the objectives of dimensionality reduction and metric learning. We propose a method that solves for the lowdimensional projection of the inputs, which minimizes a metric objective aimed at separating points in different classes by a large margin. This projection is defined by a significantly smaller number of parameters than metrics learned in input space, and thus our optimization reduces the risks of overfitting. Theory and results are presented for both a linear as well as a kernelized version of the algorithm. Overall, we achieve classification rates similar, and in several cases superior, to those of support vector machines. 1
Reidentification by relative distance comparison
 In PAMI
, 2013
"... Abstract—Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentifica ..."
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Cited by 55 (8 self)
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Abstract—Matching people across nonoverlapping camera views at different locations and different times, known as person reidentification, is both a hard and important problem for associating behavior of people observed in a large distributed space over a prolonged period of time. Person reidentification is fundamentally challenging because of the large visual appearance changes caused by variations in view angle, lighting, background clutter, and occlusion. To address these challenges, most previous approaches aim to model and extract distinctive and reliable visual features. However, seeking an optimal and robust similarity measure that quantifies a wide range of features against realistic viewing conditions from a distance is still an open and unsolved problem for person reidentification. In this paper, we formulate person reidentification as a relative distance comparison (RDC) learning problem in order to learn the optimal similarity measure between a pair of person images. This approach avoids treating all features indiscriminately and does not assume the existence of some universally distinctive and reliable features. To that end, a novel relative distance comparison model is introduced. The model is formulated to maximize the likelihood of a pair of true matches having a relatively smaller distance than that of a wrong match pair in a soft discriminant manner. Moreover, in order to maintain the tractability of the model in large scale learning, we further develop an ensemble RDC model. Extensive experiments on three publicly available benchmarking datasets are carried out to demonstrate the clear superiority of the proposed RDC models over related popular person reidentification techniques. The results also show that the new RDC models are more robust against visual appearance changes and less susceptible to model overfitting compared to other related existing models. Index Terms—Person reidentification, feature quantification, feature selection, relative distance comparison Ç 1
Adaptive relevance matrices in learning vector quantization
, 2009
"... We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototypebased classification algorithm, towards a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different feature ..."
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Cited by 55 (31 self)
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We propose a new matrix learning scheme to extend relevance learning vector quantization (RLVQ), an efficient prototypebased classification algorithm, towards a general adaptive metric. By introducing a full matrix of relevance factors in the distance measure, correlations between different features and their importance for the classification scheme can be taken into account and automated, general metric adaptation takes place during training. In comparison to the weighted Euclidean metric used in RLVQ and its variations, a full matrix is more powerful to represent the internal structure of the data appropriately. Large margin generalization bounds can be transfered to this case leading to bounds which are independent of the input dimensionality. This also holds for local metrics attached to each prototype which corresponds to piecewise quadratic decision boundaries. The algorithm is tested in comparison to alternative LVQ schemes using an artificial data set, a benchmark multiclass problem from the UCI repository, and a problem from bioinformatics, the recognition of splice sites for C.elegans.
An efficient algorithm for local distance metric learning
 in Proceedings of AAAI
, 2006
"... Learning applicationspecific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area has focused on finding metrics that simultaneously optimize compactness and separability in a global sense. Specifically, ..."
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Cited by 54 (9 self)
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Learning applicationspecific distance metrics from labeled data is critical for both statistical classification and information retrieval. Most of the earlier work in this area has focused on finding metrics that simultaneously optimize compactness and separability in a global sense. Specifically, such distance metrics attempt to keep all of the data points in each class close together while ensuring that data points from different classes are separated. However, particularly when classes exhibit multimodal data distributions, these goals conflict and thus cannot be simultaneously satisfied. This paper proposes a Local Distance Metric (LDM) that aims to optimize local compactness and local separability. We present an efficient algorithm that employs eigenvector analysis and bound optimization to learn the LDM from training data in a probabilistic framework. We demonstrate that LDM achieves significant improvements in both classification and retrieval accuracy compared to global distance learning and kernelbased KNN.
Active Learning for Large Multiclass Problems
"... Scarcity and infeasibility of human supervision for large scale multiclass classification problems necessitates active learning. Unfortunately, existing active learning methods for multiclass problems are inherently binary methods and do not scale up to a large number of classes. In this paper, we ..."
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Cited by 53 (1 self)
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Scarcity and infeasibility of human supervision for large scale multiclass classification problems necessitates active learning. Unfortunately, existing active learning methods for multiclass problems are inherently binary methods and do not scale up to a large number of classes. In this paper, we introduce a probabilistic variant of the KNearest Neighbor method for classification that can be seamlessly used for active learning in multiclass scenarios. Given some labeled training data, our method learns an accurate metric/kernel function over the input space that can be used for classification and similarity search. Unlike existing metric/kernel learning methods, our scheme is highly scalable for classification problems and provides a natural notion of uncertainty over class labels. Further, we use this measure of uncertainty to actively sample training examples that maximize discriminating capabilities of the model. Experiments on benchmark datasets show that the proposed method learns appropriate distance metrics that lead to stateoftheart performance for object categorization problems. Furthermore, our active learning method effectively samples training examples, resulting in significant accuracy gains over random sampling for multiclass problems involving a large number of classes. 1.
F.: PCCA: A new approach for distance learning from sparse pairwise constraints
 In: CVPR (2012) Reidentification: What Features Are Important? 401
"... This paper introduces Pairwise Constrained Component Analysis (PCCA), a new algorithm for learning distance metrics from sparse pairwise similarity/dissimilarity constraints in high dimensional input space, problem for which most existing distance metric learning approaches are not adapted. PCCA lea ..."
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Cited by 51 (0 self)
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This paper introduces Pairwise Constrained Component Analysis (PCCA), a new algorithm for learning distance metrics from sparse pairwise similarity/dissimilarity constraints in high dimensional input space, problem for which most existing distance metric learning approaches are not adapted. PCCA learns a projection into a lowdimensional space where the distance between pairs of data points respects the desired constraints, exhibiting good generalization properties in presence of high dimensional data. The paper also shows how to efficiently kernelize the approach. PCCA is experimentally validated on two challenging vision tasks, face verification and person reidentification, for which we obtain stateoftheart results. 1.
Descriptor Learning for Efficient Retrieval
"... Abstract. Many visual search and matching systems represent images using sparse sets of “visual words”: descriptors that have been quantized by assignment to the bestmatching symbol in a discrete vocabulary. Errors in this quantization procedure propagate throughout the rest of the system, either h ..."
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Cited by 51 (1 self)
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Abstract. Many visual search and matching systems represent images using sparse sets of “visual words”: descriptors that have been quantized by assignment to the bestmatching symbol in a discrete vocabulary. Errors in this quantization procedure propagate throughout the rest of the system, either harming performance or requiring correction using additional storage or processing. This paper aims to reduce these quantization errors at source, by learning a projection from descriptor space to a new Euclidean space in which standard clustering techniques are more likely to assign matching descriptors to the same cluster, and nonmatching descriptors to different clusters. To achieve this, we learn a nonlinear transformation model by minimizing a novel marginbased cost function, which aims to separate matching descriptors from two classes of nonmatching descriptors. Training data is generated automatically by leveraging geometric consistency. Scalable, stochastic gradient methods are used for the optimization. For the case of particular object retrieval, we demonstrate impressive gains in performance on a ground truth dataset: our learnt 32D descriptor without spatial reranking outperforms a baseline method using 128D SIFT descriptors with spatial reranking. 1
LowRank Kernel Learning with Bregman Matrix Divergences
"... In this paper, we study lowrank matrix nearness problems, with a focus on learning lowrank positive semidefinite (kernel) matrices for machine learning applications. We propose efficient algorithms that scale linearly in the number of data points and quadratically in the rank of the input matrix. E ..."
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Cited by 47 (2 self)
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In this paper, we study lowrank matrix nearness problems, with a focus on learning lowrank positive semidefinite (kernel) matrices for machine learning applications. We propose efficient algorithms that scale linearly in the number of data points and quadratically in the rank of the input matrix. Existing algorithms for learning kernel matrices often scale poorly, with running times that are cubic in the number of data points. We employ Bregman matrix divergences as the measures of nearness—these divergences are natural for learning lowrank kernels since they preserve rank as well as positive semidefiniteness. Special cases of our framework yield faster algorithms for various existing learning problems, and experimental results demonstrate that our algorithms can effectively learn both lowrank and fullrank kernel matrices.
Sparse Distance Learning for Object Recognition Combining RGB and Depth Information
"... instance recognition in the context of RGBD (depth) cameras. Motivated by local distance learning, where a novel view of an object is compared to individual views of previously seen objects, we define a viewtoobject distance where a novel view is compared simultaneously to all views of a previous ..."
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Cited by 47 (1 self)
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instance recognition in the context of RGBD (depth) cameras. Motivated by local distance learning, where a novel view of an object is compared to individual views of previously seen objects, we define a viewtoobject distance where a novel view is compared simultaneously to all views of a previous object. This novel distance is based on a weighted combination of feature differences between views. We show, through jointly learning perview weights, that this measure leads to superior classification