TY - CONF

T1 - Unsupervised learning of the set of local maxima

AU - Wolf, Lior

AU - Benaim, Sagie

AU - Galanti, Tomer

N1 - Publisher Copyright:
© 7th International Conference on Learning Representations, ICLR 2019. All Rights Reserved.

PY - 2019

Y1 - 2019

N2 - This paper describes a new form of unsupervised learning, whose input is a set of unlabeled points that are assumed to be local maxima of an unknown value function v in an unknown subset of the vector space. Two functions are learned: (i) a set indicator c, which is a binary classifier, and (ii) a comparator function h that given two nearby samples, predicts which sample has the higher value of the unknown function v. Loss terms are used to ensure that all training samples x are a local maxima of v, according to h and satisfy c(x) = 1. Therefore, c and h provide training signals to each other: a point x0 in the vicinity of x satisfies c(x) = −1 or is deemed by h to be lower in value than x. We present an algorithm, show an example where it is more efficient to use local maxima as an indicator function than to employ conventional classification, and derive a suitable generalization bound. Our experiments show that the method is able to outperform one-class classification algorithms in the task of anomaly detection and also provide an additional signal that is extracted in a completely unsupervised way.

AB - This paper describes a new form of unsupervised learning, whose input is a set of unlabeled points that are assumed to be local maxima of an unknown value function v in an unknown subset of the vector space. Two functions are learned: (i) a set indicator c, which is a binary classifier, and (ii) a comparator function h that given two nearby samples, predicts which sample has the higher value of the unknown function v. Loss terms are used to ensure that all training samples x are a local maxima of v, according to h and satisfy c(x) = 1. Therefore, c and h provide training signals to each other: a point x0 in the vicinity of x satisfies c(x) = −1 or is deemed by h to be lower in value than x. We present an algorithm, show an example where it is more efficient to use local maxima as an indicator function than to employ conventional classification, and derive a suitable generalization bound. Our experiments show that the method is able to outperform one-class classification algorithms in the task of anomaly detection and also provide an additional signal that is extracted in a completely unsupervised way.

UR - http://www.scopus.com/inward/record.url?scp=85083951694&partnerID=8YFLogxK

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AN - SCOPUS:85083951694

T2 - 7th International Conference on Learning Representations, ICLR 2019

Y2 - 6 May 2019 through 9 May 2019

ER -