Batch steepest-descent-mildest-ascent for interactive maximum margin clustering

TY - GEN

T1 - Batch steepest-descent-mildest-ascent for interactive maximum margin clustering

AU - Gieseke, Fabian

AU - Pahikkala, Tapio

AU - Heskes, Tom

N1 - Conference code: 14

PY - 2015

Y1 - 2015

N2 - The maximum margin clustering principle extends support vector machines to unsupervised scenarios. We present a variant of this clustering scheme that can be used in the context of interactive clustering scenarios. In particular, our approach permits the class ratios to be manually defined by the user during the fitting process. Our framework can be used at early stages of the data mining process when no or very little information is given about the true clusters and class ratios. One of the key contributions is an adapted steepest-descent-mildest-ascent optimization scheme that can be used to fine-tune maximum margin clustering solutions in an interactive manner. We demonstrate the applicability of our approach in the context of remote sensing and astronomy with training sets consisting of hundreds of thousands of patterns.

AB - The maximum margin clustering principle extends support vector machines to unsupervised scenarios. We present a variant of this clustering scheme that can be used in the context of interactive clustering scenarios. In particular, our approach permits the class ratios to be manually defined by the user during the fitting process. Our framework can be used at early stages of the data mining process when no or very little information is given about the true clusters and class ratios. One of the key contributions is an adapted steepest-descent-mildest-ascent optimization scheme that can be used to fine-tune maximum margin clustering solutions in an interactive manner. We demonstrate the applicability of our approach in the context of remote sensing and astronomy with training sets consisting of hundreds of thousands of patterns.

U2 - 10.1007/978-3-319-24465-5_9

DO - 10.1007/978-3-319-24465-5_9

M3 - Article in proceedings

SN - 978-3-319-24464-8

T3 - Lecture notes in computer science

SP - 95

EP - 107

BT - Advances in Intelligent Data Analysis XIV

A2 - Fromont, Elisa

A2 - De Bie, Tijl

A2 - van Leeuwen, Matthijs

PB - Springer

T2 - 14th International Symposium on Advances in Intelligent Data Analysis

Y2 - 22 October 2015 through 24 October 2015

ER -