Semi-supervised classification with privileged information

Qi, Zhiquan; Tian, Yingjie; Niu, Lingfeng; Wang, Bo

doi:10.1007/s13042-015-0390-1

Cited by 8 publications

(2 citation statements)

References 10 publications

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“…These mechanisms can be used for accelerating the speed of learning. In Qi et al (2015), a semi-supervised learning approach using privileged information is proposed. This approach can exploit both the distribution information in unlabeled data and privileged information, to improve the efficiency of the learning.…”

Section: Related Workmentioning

confidence: 99%

On the Capacity Limits of Privileged ERM

Sharoni¹,

Sabato²

2023

Preprint

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We study the supervised learning paradigm called Learning Using Privileged Information, first suggested by Vapnik and Vashist (2009). In this paradigm, in addition to the examples and labels, additional (privileged) information is provided only for training examples. The goal is to use this information to improve the classification accuracy of the resulting classifier, where this classifier can only use the non-privileged information of new example instances to predict their label. We study the theory of privileged learning with the zero-one loss under the natural Privileged ERM algorithm proposed in Pechyony and Vapnik (2010a). We provide a counter example to a claim made in that work regarding the VC dimension of the loss class induced by this problem; We conclude that the claim is incorrect. We then provide a correct VC dimension analysis which gives both lower and upper bounds on the capacity of the Privileged ERM loss class. We further show, via a generalization analysis, that worst-case guarantees for Privileged ERM cannot improve over standard non-privileged ERM, unless the capacity of the privileged information is similar or smaller to that of the non-privileged information. This result points to an important limitation of the Privileged ERM approach. In our closing discussion, we suggest another way in which Privileged ERM might still be helpful, even when the capacity of the privileged information is large.

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Section: Related Workmentioning

confidence: 99%

On the Capacity Limits of Privileged ERM

Sharoni¹,

Sabato²

2023

Preprint

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“…In our future work, we intend to pursue extensive empirical experiments in order to compare the proposed self-labeled method AAST with various methods, belonging to other SSL classes such as generative mixture models [14,41], transductive SVMs [42][43][44], graph-based methods [45][46][47][48][49], extreme learning methods [50][51][52], expectation maximization with generative mixture models [14,53]. Furthermore, since our experimental results are quite encouraging, our next step is the use of other supervised classifiers as base learners, such as neural networks [54] and support vector machines [55] or ensemble-based learners [26] aiming to enhance our proposed framework with more sophisticated and theoretically motivated selection criteria for the most promising classifier in order to study the behavior of AAST at each cycle.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

An Auto-Adjustable Semi-Supervised Self-Training Algorithm

et al. 2018

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Semi-supervised learning algorithms have become a topic of significant research as an alternative to traditional classification methods which exhibit remarkable performance over labeled data but lack the ability to be applied on large amounts of unlabeled data. In this work, we propose a new semi-supervised learning algorithm that dynamically selects the most promising learner for a classification problem from a pool of classifiers based on a self-training philosophy. Our experimental results illustrate that the proposed algorithm outperforms its component semi-supervised learning algorithms in terms of accuracy, leading to more efficient, stable and robust predictive models.

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