TY - GEN

T1 - Metric embedding for kernel classification rules

AU - Sriperumbudur, Bharath K.

AU - Lang, Omer A.

AU - Lanckriet, Gert R.G.

N1 - Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

PY - 2008

Y1 - 2008

N2 - In this paper, we consider a smoothing kernel based classification rule and propose an algorithm for optimizing the performance of the rule by learning the bandwidth of the smoothing kernel along with a data-dependent distance metric. The data-dependent distance metric is obtained by learning a function that embeds an arbitrary metric space into a Euclidean space while minimizing an upper bound on the resubstitution estimate of the error probability of the kernel classification rule. By restricting this embedding function to a reproducing kernel Hubert space, we reduce the problem to solving a semidefinite program and show the resulting kernel classification rule to be a variation of the k-nearest neighbor rule. We compare the performance of the kernel rule (using the learned data-dependent distance metric) to state-of-the-art distance metric learning algorithms (designed for k-nearest neighbor classification) on some benchmark datasets. The results show that the proposed rule has either better or as good classification accuracy as the other metric learning algorithms.

AB - In this paper, we consider a smoothing kernel based classification rule and propose an algorithm for optimizing the performance of the rule by learning the bandwidth of the smoothing kernel along with a data-dependent distance metric. The data-dependent distance metric is obtained by learning a function that embeds an arbitrary metric space into a Euclidean space while minimizing an upper bound on the resubstitution estimate of the error probability of the kernel classification rule. By restricting this embedding function to a reproducing kernel Hubert space, we reduce the problem to solving a semidefinite program and show the resulting kernel classification rule to be a variation of the k-nearest neighbor rule. We compare the performance of the kernel rule (using the learned data-dependent distance metric) to state-of-the-art distance metric learning algorithms (designed for k-nearest neighbor classification) on some benchmark datasets. The results show that the proposed rule has either better or as good classification accuracy as the other metric learning algorithms.

UR - http://www.scopus.com/inward/record.url?scp=56449095464&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=56449095464&partnerID=8YFLogxK

U2 - 10.1145/1390156.1390283

DO - 10.1145/1390156.1390283

M3 - Conference contribution

AN - SCOPUS:56449095464

SN - 9781605582054

T3 - Proceedings of the 25th International Conference on Machine Learning

SP - 1008

EP - 1015

BT - Proceedings of the 25th International Conference on Machine Learning

PB - Association for Computing Machinery (ACM)

T2 - 25th International Conference on Machine Learning

Y2 - 5 July 2008 through 9 July 2008

ER -