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58
Transductive Component Analysis
 Proc. IEEE Int’l Conf. Data Mining
, 2008
"... Precession missile feature extraction using sparse ..."
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Precession missile feature extraction using sparse
Online Learning in the Embedded Manifold of Lowrank Matrices
"... When learning models that are represented in matrix forms, enforcing a lowrank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model. However, naive approaches to minimizing functions over the set of lowrank matrices are eithe ..."
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When learning models that are represented in matrix forms, enforcing a lowrank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model. However, naive approaches to minimizing functions over the set of lowrank matrices are either prohibitively time consuming (repeated singular value decomposition of the matrix) or numerically unstable (optimizing a factored representation of the lowrank matrix). We build on recent advances in optimization over manifolds, and describe an iterative online learning procedure, consisting of a gradient step, followed by a secondorder retraction back to the manifold. While the ideal retraction is costly to compute, and so is the projection operator that approximates it, we describe another retraction that can be computed efficiently. It has run time and memory complexity of O((n+m)k) for a rankk matrix of dimension m×n, when using an online procedure with rankone gradients. We use this algorithm, LORETA, to learn a matrixform similarity measure over pairs of documents represented as high dimensional vectors. LORETA improves the mean average precision over a passiveaggressive approach in a factorized model, and also improves over a full model trained on preselected features using the same memory requirements. We further adapt LORETA to learn positive semidefinite lowrank matrices, providing an online algorithm for lowrank metric learning. LORETA also shows consistent improvement over standard weakly supervised methods in a large (1600 classes and 1 million images, using ImageNet) multilabel image classification task.
CueT: HumanGuided Fast and Accurate Network Alarm Triage
"... Network alarm triage refers to grouping and prioritizing a stream of lowlevel device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing tools cannot easily evolve with the network. We present CueT, a system that uses interacti ..."
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Cited by 13 (4 self)
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Network alarm triage refers to grouping and prioritizing a stream of lowlevel device health information to help operators find and fix problems. Today, this process tends to be largely manual because existing tools cannot easily evolve with the network. We present CueT, a system that uses interactive machine learning to learn from the triaging decisions of operators. It then uses that learning in novel visualizations to help them quickly and accurately triage alarms. Unlike prior interactive machine learning systems, CueT handles a highly dynamic environment where the groups of interest are not known apriori and evolve constantly. A user study with real operators and data from a large network shows that CueT significantly improves the speed and accuracy of alarm triage compared to the network’s current practice.
Learning binary hash codes for largescale image search
 MACHINE LEARNING FOR COMPUTER VISION
, 2013
"... Algorithms to rapidly search massive image or video collections are critical for many vision applications, including visual search, contentbased retrieval, and nonparametric models for object recognition. Recent work shows that learned binary projections are a powerful way to index large collect ..."
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Algorithms to rapidly search massive image or video collections are critical for many vision applications, including visual search, contentbased retrieval, and nonparametric models for object recognition. Recent work shows that learned binary projections are a powerful way to index large collections according to their content. The basic idea is to formulate the projections so as to approximately preserve a given similarity function of interest. Having done so, one can then search the data efficiently using hash tables, or by exploring the Hamming ball volume around a novel query. Both enable sublinear time retrieval with respect to the database size. Further, depending on the design of the projections, in some cases it is possible to bound the number of database examples that must be searched in order to achieve a given level of accuracy. This chapter overviews data structures for fast search with binary codes, and then describes several supervised and unsupervised strategies for generating the codes. In particular, we review supervised methods that integrate metric learning, boosting, and neural networks into the hash key construction, and unsupervised methods based on spectral analysis or kernelized random projections that compute affinitypreserving binary codes. Whether learning from explicit semantic supervision or exploiting the structure among unlabeled data, these methods make scalable retrieval possible for a variety of robust visual similarity measures. We focus on defining the algorithms, and illustrate the main points with results using millions of images.
Online Learning in the Manifold of LowRank Matrices
"... When learning models that are represented in matrix forms, enforcing a lowrank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model. However, naive approaches for minimizing functions over the set of lowrank matrices are eith ..."
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Cited by 9 (0 self)
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When learning models that are represented in matrix forms, enforcing a lowrank constraint can dramatically improve the memory and run time complexity, while providing a natural regularization of the model. However, naive approaches for minimizing functions over the set of lowrank matrices are either prohibitively time consuming (repeated singular value decomposition of the matrix) or numerically unstable (optimizing a factored representation of the low rank matrix). We build on recent advances in optimization over manifolds, and describe an iterative online learning procedure, consisting of a gradient step, followed by a secondorder retraction back to the manifold. While the ideal retraction is hard to compute, and so is the projection operator that approximates it, we describe another secondorder retraction that can be computed efficiently, with run time and memory complexity of O ((n + m)k) for a rankk matrix of dimension m × n, given rankone gradients. We use this algorithm, LORETA, to learn a matrixform similarity measure over pairs of documents represented as high dimensional vectors. LORETA improves the mean average precision over a passive aggressive approach in a factorized model, and also improves over a full model trained over preselected features using the same memory requirements. LORETA also showed consistent improvement over standard methods in a large (1600 classes) multilabel image classification task. 1
Vision of a visipedia
 Proceedings of the IEEE
, 2010
"... The web is not perfect: while text is easily searched and organized, pictures (the vast majority of the bits that one can find online) are not. In order to see how one could improve the web and make pictures firstclass citizens of the web, I explore the idea of Visipedia, a visual interface for Wi ..."
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The web is not perfect: while text is easily searched and organized, pictures (the vast majority of the bits that one can find online) are not. In order to see how one could improve the web and make pictures firstclass citizens of the web, I explore the idea of Visipedia, a visual interface for Wikipedia that is able to answer visual queries and enables experts to contribute and organize visual knowledge. Four distinct groups of humans would interact through Visipedia: users, experts, visual workers and machine vision scientists. The latter would gradually build automata able to interpret images. I explore some of the technical challenges involved in making Visipedia happen. I argue that Visipedia will likely grow organically, combining stateoftheart machine vision with human labor.
Similarity Learning for Provably Accurate Sparse Linear Classification
 In ICML
, 2012
"... In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions. Most of the state of the art focus on learning Mahalanobis distances (requiring to fulfill a constraint of positive semidefiniteness) ..."
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In recent years, the crucial importance of metrics in machine learning algorithms has led to an increasing interest for optimizing distance and similarity functions. Most of the state of the art focus on learning Mahalanobis distances (requiring to fulfill a constraint of positive semidefiniteness) for use in a local kNN algorithm. However, no theoretical link is established between the learned metrics and their performance in classification. In this paper, we make use of the formal framework of (ǫ,γ,τ)good similarities introduced by Balcan et al. to design an algorithm for learning a non PSD linear similarity optimized in a nonlinear feature space, which is then used to build a global linear classifier. We show that our approach has uniform stability and derive a generalization bound on the classification error. Experiments performed on various datasets confirm the effectiveness of our approach compared to stateoftheart methods and provide evidence that (i) it is fast, (ii) robust to overfitting and (iii) produces very sparse classifiers. 1.
Online visual vocabulary pruning using pairwise constraints
 in Proc. IEEE Conf. Comput. Vis. Pattern Recognit
"... Given a pair of images represented using bagofvisualwordsandalabelcorrespondingtowhethertheimagesare “related”(mustlink constraint) or “unrelated ” (cannotlink constraint),we addressthe problemof selectingasubsetofvisualwordsthataresalientinexplainingtherelation between the image pair. In particu ..."
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Given a pair of images represented using bagofvisualwordsandalabelcorrespondingtowhethertheimagesare “related”(mustlink constraint) or “unrelated ” (cannotlink constraint),we addressthe problemof selectingasubsetofvisualwordsthataresalientinexplainingtherelation between the image pair. In particular, a subset of features isselectedsuchthatthedistancecomputedusingthesefeatures satisfies the given pairwise constraints. An efficient onlinefeatureselectionalgorithmispresentedbasedonthe dualgradient descent approach. Side information in the form of pairwise constraints is incorporated into the feature selection stage, providing the user with flexibility to useanunsupervisedorsemisupervisedalgorithmatalater stage. Correlated subsets of visual words, usually resulting from hierarchical quantizationprocess (called groups), are exploited to select a significantly smaller vocabulary. A groupLASSO regularizer is used to drive as many feature weights to zero as possible. We evaluate the quality of the pruned vocabulary by clustering the data using the resulting feature subset. Experimentson PASCAL VOC 2007 datasetusing5000visualkeywords,resultedinaround80% reduction in the number of keywords, with little or no loss inperformance.
Collaborative Personalization of Image Enhancement
"... While most existing enhancement tools for photographs have universal autoenhancement functionality, recent research [8] shows that users can have personalized preferences. In this paper, we explore whether such personalized preferences in image enhancement tend to cluster and whether users can be g ..."
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While most existing enhancement tools for photographs have universal autoenhancement functionality, recent research [8] shows that users can have personalized preferences. In this paper, we explore whether such personalized preferences in image enhancement tend to cluster and whether users can be grouped according to such preferences. To this end, we analyze a comprehensive data set of image enhancements collected from 336 users via Amazon Mechanical Turk. We find that such clusters do exist and can be used to derive methods to learn statistical preference models from a group of users. We also present a probabilistic framework that exploits the ideas behind collaborative filtering to automatically enhance novel images for new users. Experiments show that inferring clusters in image enhancement preferences results in better prediction of image enhancement preferences and outperforms generic autocorrection tools. 1.
Deep learning for contentbased image retrieval: A comprehensive study
 In ACM Multimedia
, 2014
"... Learning effective feature representations and similarity measures are crucial to the retrieval performance of a contentbased image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes o ..."
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Learning effective feature representations and similarity measures are crucial to the retrieval performance of a contentbased image retrieval (CBIR) system. Despite extensive research efforts for decades, it remains one of the most challenging open problems that considerably hinders the successes of realworld CBIR systems. The key challenge has been attributed to the wellknown “semantic gap ” issue that exists between lowlevel image pixels captured by machines and highlevel semantic concepts perceived by human. Among various techniques, machine learning has been actively investigated as a possible direction to bridge the semantic gap in the long term. Inspired by recent successes of deep learning techniques for computer vision and other applications, in this paper, we attempt to address an open problem: if deep learning is a hope for bridging the semantic gap in CBIR and how much improvements in