Generalized Roof Duality for Multi-Label Optimization: Optimal Lower Bounds and Persistency

Windheuser, Thomas; Ishikawa, Hiroshi; Cremers, Daniel

doi:10.1007/978-3-642-33783-3_29

Cited by 17 publications

(16 citation statements)

References 13 publications

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“…[27] predicted the number of relevant labels for each instance by a logistic regression, and then adopted the SVM models to minimize the expected loss for the instance selection. Recently, [12] adopted the mutual information to design the selection criterion for Bayesian multi-label active learning, and [28] selected the valuable instances by minimizing the empirical risk. Other works have been done by combining the informativeness and representativeness together for a better query [29], [30].…”

Section: Related Workmentioning

confidence: 99%

Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion

Wang

Zhang

et al. 2017

IEEE Trans. on Image Process.

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Multi-label learning draws great interests in many real world applications. It is a highly costly task to assign many labels by the oracle for one instance. Meanwhile, it is also hard to build a good model without diagnosing discriminative labels. Can we reduce the label costs and improve the ability to train a good model for multi-label learning simultaneously? Active learning addresses the less training samples problem by querying the most valuable samples to achieve a better performance with little costs. In multi-label active learning, some researches have been done for querying the relevant labels with less training samples or querying all labels without diagnosing the discriminative information. They all cannot effectively handle the outlier labels for the measurement of uncertainty. Since maximum correntropy criterion (MCC) provides a robust analysis for outliers in many machine learning and data mining algorithms, in this paper, we derive a robust multi-label active learning algorithm based on an MCC by merging uncertainty and representativeness, and propose an efficient alternating optimization method to solve it. With MCC, our method can eliminate the influence of outlier labels that are not discriminative to measure the uncertainty. To make further improvement on the ability of information measurement, we merge uncertainty and representativeness with the prediction labels of unknown data. It cannot only enhance the uncertainty but also improve the similarity measurement of multi-label data with labels information. Experiments on benchmark multi-label data sets have shown a superior performance than the state-of-the-art methods.

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Section: Related Workmentioning

confidence: 99%

Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion

Wang

Zhang

et al. 2017

IEEE Trans. on Image Process.

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“…A popular approach to the inference problem is to find the optimal labeling for a subset of the variables [8,13,18,20,34,35,36,39,44]. A partial labeling that holds in every global minimizer is said to be persistent [3].…”

Section: Related Workmentioning

confidence: 99%

A Discriminative View of MRF Pre-processing Algorithms

Wang

Herrmann

Zabih

2017

2017 IEEE International Conference on Computer Vision (ICCV)

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While Markov Random Fields (MRFs) are widely used in computer vision, they present a quite challenging inference problem. MRF inference can be accelerated by preprocessing techniques like Dead End Elimination (DEE) [8] or 24,25] which compute the optimal labeling of a subset of variables. These techniques are guaranteed to never wrongly label a variable but they often leave a large number of variables unlabeled. We address this shortcoming by interpreting pre-processing as a classification problem, which allows us to trade off false positives (i.e., giving a variable an incorrect label) versus false negatives (i.e., failing to label a variable). We describe an efficient discriminative rule that finds optimal solutions for a subset of variables. Our technique provides both perinstance and worst-case guarantees concerning the quality of the solution. Empirical studies were conducted over several benchmark datasets. We obtain a speedup factor of 2 to 12 over expansion moves [4] without preprocessing, and on difficult non-submodular energy functions produce slightly lower energy.1 See [12,29] for rare counterexamples.

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“…The task of finding persistent variables in labeling problems has been studied and many approaches have been proposed [5,9,10,13,14,16,17,20]. To our best knowledge the earliest paper concerning itself with persistency is [16], which states a persistency criterion for the stable set problem and verifies it for every solution of a certain relaxation, which the roof duality method in [5] uses and which is also the basis for the well known QPBO-algorithm [5,17].…”

Section: Related Work and Contributionmentioning

confidence: 99%

“…To our best knowledge the earliest paper concerning itself with persistency is [16], which states a persistency criterion for the stable set problem and verifies it for every solution of a certain relaxation, which the roof duality method in [5] uses and which is also the basis for the well known QPBO-algorithm [5,17]. Roof Duality has been extended for Multi-Label problems in [13,20] and for higher order binary problems in [10]. A different approach, specialized for Potts models, is pursued in [14], where possible labelings are tested for persistency.…”

Section: Related Work and Contributionmentioning

confidence: 99%

Partial Optimality via Iterative Pruning for the Potts Model

Swoboda

Savchynskyy

Kappes

et al. 2013

Lecture Notes in Computer Science

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Abstract. We propose a novel method to obtain a part of an optimal non-relaxed integral solution for energy minimization problems with Potts interactions, known also as the minimal partition problem. The method empirically outperforms previous approaches like MQPBO and Kovtun's method in most of our test instances and especially in hard ones. As a starting point our approach uses the solution of a commonly accepted convex relaxation of the problem. This solution is then iteratively pruned until our criterion for partial optimality is satisfied. Due to its generality our method can employ any solver for the considered relaxed problem.

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Generalized Roof Duality for Multi-Label Optimization: Optimal Lower Bounds and Persistency

Cited by 17 publications

References 13 publications

Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion

Robust and Discriminative Labeling for Multi-Label Active Learning Based on Maximum Correntropy Criterion

A Discriminative View of MRF Pre-processing Algorithms

Partial Optimality via Iterative Pruning for the Potts Model

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