AdaBoost on low-rank PSD matrices for metric learning

Bi, Jinbo; Wu, Dijia; Lü, Le; Liu, Meizhu; Tao, Yimo; Wolf, Matthias

doi:10.1109/cvpr.2011.5995363

Cited by 28 publications

(20 citation statements)

References 19 publications

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“…Such an ensemble of weak learners has been proven to be more powerful than a single complex classifier and has better generalization performance [7]. In [8] and [9], a boosting algorithm has been implemented for learning a full distance metric, which has motivated the proposed algorithm in this paper. Their important theorem on trace-one semi-definite matrices is central to the theoretical basis of our approach.…”

Section: Metric Learning Via Boostingmentioning

confidence: 99%

Boosted sparse nonlinear distance metric learning

Tian

2016

Statistical Analysis

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This paper proposes a boosting-based solution addressing metric learning problems for high-dimensional data. Distance measures have been used as natural measures of (dis)similarity and served as the foundation of various learning methods. The efficiency of distance-based learning methods heavily depends on the chosen distance metric. With increasing dimensionality and complexity of data, however, traditional metric learning methods suffer from poor scalability and the limitation due to linearity as the true signals are usually embedded within a low-dimensional nonlinear subspace. In this paper, we propose a nonlinear sparse metric learning algorithm via boosting. We restructure a global optimization problem into a forward stage-wise learning of weak learners based on a rank-one decomposition of the weight matrix in the Mahalanobis distance metric. A gradient boosting algorithm is devised to obtain a sparse rank-one update of the weight matrix at each step. Nonlinear features are learned by a hierarchical expansion of interactions incorporated within the boosting algorithm. Meanwhile, an early stopping rule is imposed to control the overall complexity of the learned metric. As a result, our approach guarantees three desirable properties of the final metric: positive semi-definiteness, low rank and element-wise sparsity. Numerical experiments show that our learning model compares favorably with the state-of-the-art methods in the current literature of metric learning.

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Section: Metric Learning Via Boostingmentioning

confidence: 99%

Boosted sparse nonlinear distance metric learning

Tian

2016

Statistical Analysis

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“…The first and second weak learners are complementary as they use different face regions for measuring distances and the resulting same labeled pairs with large distances are different. on AR-Scarf and also comparative convergence results with BoostMetric [29] and AdaboostMetric [4]. In the following, ESPAC-NS denotes the proposed method with no feature selection.…”

Section: Visualization Of Sparse Structures Ofmentioning

confidence: 99%

“…Therefore, rank-one PSD matrices parameterized weak metrics are learned sequentially. In [4], Bi et al proposed to learn Mahalanobis metrics in an Adaboost manner.…”

Section: Single-modal Metric Learningmentioning

confidence: 99%

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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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“…Recent works such as have shown how interesting it is to learn an optimal metric for a particular task using Metric Learning (ML). Most approaches learn a Mahalanobis metric based on an objective function whose constraints comes either from a set of labelled examples or, more frequently, from sets of positive (same class) and negative (different class) pairs.In the recent works that have used metric learning (ML), the works of Shen et al [4] and Bi et al [2], introducing algorithms based on Boosting approach, deserves a particular attention due to the interesting properties they offers: i) they are efficient and scalable, as no semidefinite programming is required, at each iteration only the largest eigenvalue and corresponding eigenvectors are needed, ii) Like AdaBoost, they don't have any parameter to tune and is easy to implement as no sophisticated optimization techniques are involved. It hence contrasts with most of the commonly used ML methods for which hyper-parameters, often introduced for regularization purpose, have to be adjusted by cross-validation.…”

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confidence: 99%

Boosted Metric Learning for Efficient Identity-Based Face Retrieval

Negrel

Lechervy

Jurie

2015

Procedings of the British Machine Vision Conference 2015

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The performance of many visual recognition algorithms -e.g. face verification, person re-identification -heavily depends on the metric used to measure the similarity between input data. Recent works such as have shown how interesting it is to learn an optimal metric for a particular task using Metric Learning (ML). Most approaches learn a Mahalanobis metric based on an objective function whose constraints comes either from a set of labelled examples or, more frequently, from sets of positive (same class) and negative (different class) pairs.In the recent works that have used metric learning (ML), the works of Shen et al. [4] and Bi et al. [2], introducing algorithms based on Boosting approach, deserves a particular attention due to the interesting properties they offers: i) they are efficient and scalable, as no semidefinite programming is required, at each iteration only the largest eigenvalue and corresponding eigenvectors are needed, ii) Like AdaBoost, they don't have any parameter to tune and is easy to implement as no sophisticated optimization techniques are involved. It hence contrasts with most of the commonly used ML methods for which hyper-parameters, often introduced for regularization purpose, have to be adjusted by cross-validation.However, one strong limitation of these approaches [2, 4] is that it requires having triplets for learning the metric i.e. constraints expressed under the form D(x a , x b ) < D(x a , x c ) where x a , x b , x c are three input vector for which the label is known: positive and negative pairs within a constraint do have to share a common vector. This is a limitation as for most of the verification task only training pairs are available (e.g. for person re-identification on the Viper dataset only pairs of same/different persons are provided for training, and thus using such triplets is not possible).One of the key contribution of this paper is to propose a metric learning approach based on boosting allowing to use pairs of points for training i.e. which can use constraints such as D(x a , x b ) < D(x c , x d ) while keeping the good properties of [4] (scalability, simplicity, no parameters, etc.). Our approach is based on the ranking algorithm RankBoost [3], known to offer 3 particularly interesting features: i) no hyperparameter, ii) great robustness to overfitting (explained with a standard VC-dimensional analysis techniques), and iii) a computational trick for reducing the complexity of the learning step. In the following, we show how to build on RankBoost [3] to efficiently address this metric learning problem.Moreover, we propose a method for building hierarchical face representations for efficient identity face retrieval task. The main idea is to use a ML algorithm for building a tree structured representation of a large set of faces, such that identical-identity faces belongs to the same cluster at each tree level. The hierarchical clustering is built recursively: at each level, a new metric is learned using the faces belonging to a particular tree node which is then...

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AdaBoost on low-rank PSD matrices for metric learning

Cited by 28 publications

References 19 publications

Boosted sparse nonlinear distance metric learning

Boosted sparse nonlinear distance metric learning

Ensemble of Sparse Cross-Modal Metrics for Heterogeneous Face Recognition

Boosted Metric Learning for Efficient Identity-Based Face Retrieval

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