Metric Learning

Bellet, Aurélien; Habrard, Amaury; Sebban, Marc

doi:10.2200/s00626ed1v01y201501aim030

Cited by 108 publications

(112 citation statements)

References 172 publications

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“…[30,31,32]. However, only few approaches exist for structure metrics and alignment distances in particular, making it a novel and challenging field of machine learning research [30,33,11].…”

Section: Structure Metric Learningmentioning

confidence: 99%

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Adaptive structure metrics for automated feedback provision in intelligent tutoring systems

Paaßen

Hammer

2016

Neurocomputing

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Typical intelligent tutoring systems rely on detailed domain-knowledge which is hard to obtain and difficult to encode. As a data-driven alternative to explicit domain-knowledge, one can present learners with feedback based on similar existing solutions from a set of stored examples. At the heart of such a data-driven approach is the notion of similarity. We present a general-purpose framework to construct structure metrics on sequential data and to adapt those metrics using machine learning techniques. We demonstrate that metric adaptation improves the classification of wrong versus correct learner attempts in a simulated data set from sports training, and the classification of the underlying learner strategy in a real Java programming dataset.

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Section: Structure Metric Learningmentioning

confidence: 99%

“…This formulation is inspired by the generalized quadratic form metric in vectorial settings [31,30,32]:…”

Section: Metric Learning Using Rglvqmentioning

confidence: 99%

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Adaptive structure metrics for automated feedback provision in intelligent tutoring systems

Paaßen

Hammer

2016

Neurocomputing

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“…For learning a similarity measurement, metric learning is widely used [3]. However the currently widely used metric learning methods are Mahalanobis distance based.…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Sparse Cross-Modal Metrics for Heterogeneous Face Recognition

Huo

Shi

Yang

et al. 2016

Proceedings of the 24th ACM International Conference on Multimedia

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Heterogeneous face recognition aims to identify or verify person identity by matching facial images of different modalities. In practice, it is known that its performance is highly influenced by modality inconsistency, appearance occlusions, illumination variations and expressions. In this paper, a new method named as ensemble of sparse cross-modal metrics is proposed for tackling these challenging issues. In particular, a weak sparse cross-modal metric learning method is firstly developed to measure distances between samples of two modalities. It learns to adjust rank-one cross-modal metrics to satisfy two sets of triplet based cross-modal distance constraints in a compact form. Meanwhile, a group based feature selection is performed to enforce that features in the same position of two modalities are selected simultaneously. By neglecting features that attribute to "noise" in the face regions (eye glasses, expressions and so on), the performance of learned weak metrics can be markedly improved. Finally, an ensemble framework is incorporated to combine the results of differently learned sparse metrics into a strong one. Extensive experiments on various face datasets demonstrate the benefit of such feature selection especially when heavy occlusions exist. The proposed ensemble metric learning has been shown superiority over several state-of-the-art methods in heterogeneous face recognition.

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“…Several methods have been proposed to learn projections that are capable of reducing the size of the signatures while preserving their performance. Most of these approaches are based on metric leaning algorithms [1] used to learn Mahalanobis-like distances:…”

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confidence: 99%

MLBoost Revisited: A Faster Metric Learning Algorithm for Identity-Based Face Retrieval

Negrel¹,

Lechervy²,

Jurie³

2016

Procedings of the British Machine Vision Conference 2016

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This paper focuses on the problem of identitybased face retrieval [2], a problem heavily depending on the quality of the similarity function used to compare the images. Instead of using standard or handcrafted similarity functions, one of the most popular ways to address this problem is to learn adapted metrics, from sets of similar and dissimilar example pairs. This is generally equivalent to projecting the face signatures into an adapted (possibly low-dimensional) space in which the similarity can be measured by the Euclidean distance. For large scale applications, the dimension of this subspace should be as small as possible to limit the storage requirements, and the projections should be fast to compute. Since the Euclidean distance fulfill the second requirement, producing face representations adapted to the Euclidean metric is interesting. However, such representations usually have very large sizes. Several methods have been proposed to learn projections that are capable of reducing the size of the signatures while preserving their performance. Most of these approaches are based on metric leaning algorithms [1] used to learn Mahalanobis-like distances:with W a positive semi-definite matrix. To guarantee this property and to reduce the size of the signatures, these methods use the factorization W = LL with L ∈ M D×d as projection matrix: y i = L x i . It is important to control the rank of W so that the dimension of the reduced signature is as small as possible.In this paper, we focus on a particular metric learning algorithm so-called MLBoost [3], a supervised method based on boosting. MLBoost learns the metric incrementally by aggregating several weak metrics: with α (t) the weights of the weak metrics and z (t) the projector vectors of these weak metrics.Here, we propose two improvements over MLBoost [3]: (i) A new method for computing weak metrics at a lower computational cost;

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Metric Learning

Cited by 108 publications

References 172 publications

Adaptive structure metrics for automated feedback provision in intelligent tutoring systems

Adaptive structure metrics for automated feedback provision in intelligent tutoring systems

Ensemble of Sparse Cross-Modal Metrics for Heterogeneous Face Recognition

MLBoost Revisited: A Faster Metric Learning Algorithm for Identity-Based Face Retrieval

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