Exemplar Based Mixture Models with Censored Data

Masahiro Kohjima, Tatsushi Matsubayashi, Hiroyuki Toda
Proceedings of The Eleventh Asian Conference on Machine Learning, PMLR 101:535-550, 2019.

Abstract

In this paper, we propose a method that can handle censored data, data collected under the condition that the exact value is recorded only when the value is within a certain range, abbreviated information is recorded otherwise. It is known that existing methods that use mixture models with censored data suffer from (i) the existence of local optimum solutions and (ii) the need to compute the statistics of truncated distributions for parameter estimation. Our proposal, exemplar based censored mixture model (EBCM), overcomes these two difficulties at once by adopting the exemplar based model approach. The effectiveness of EBCM is confirmed by experiments on synthetic and real world dat sets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v101-kohjima19a, title = {Exemplar Based Mixture Models with Censored Data}, author = {Kohjima, Masahiro and Matsubayashi, Tatsushi and Toda, Hiroyuki}, booktitle = {Proceedings of The Eleventh Asian Conference on Machine Learning}, pages = {535--550}, year = {2019}, editor = {Lee, Wee Sun and Suzuki, Taiji}, volume = {101}, series = {Proceedings of Machine Learning Research}, month = {17--19 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v101/kohjima19a/kohjima19a.pdf}, url = {https://proceedings.mlr.press/v101/kohjima19a.html}, abstract = {In this paper, we propose a method that can handle censored data, data collected under the condition that the exact value is recorded only when the value is within a certain range, abbreviated information is recorded otherwise. It is known that existing methods that use mixture models with censored data suffer from (i) the existence of local optimum solutions and (ii) the need to compute the statistics of truncated distributions for parameter estimation. Our proposal, exemplar based censored mixture model (EBCM), overcomes these two difficulties at once by adopting the exemplar based model approach. The effectiveness of EBCM is confirmed by experiments on synthetic and real world dat sets.} }
Endnote
%0 Conference Paper %T Exemplar Based Mixture Models with Censored Data %A Masahiro Kohjima %A Tatsushi Matsubayashi %A Hiroyuki Toda %B Proceedings of The Eleventh Asian Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Wee Sun Lee %E Taiji Suzuki %F pmlr-v101-kohjima19a %I PMLR %P 535--550 %U https://proceedings.mlr.press/v101/kohjima19a.html %V 101 %X In this paper, we propose a method that can handle censored data, data collected under the condition that the exact value is recorded only when the value is within a certain range, abbreviated information is recorded otherwise. It is known that existing methods that use mixture models with censored data suffer from (i) the existence of local optimum solutions and (ii) the need to compute the statistics of truncated distributions for parameter estimation. Our proposal, exemplar based censored mixture model (EBCM), overcomes these two difficulties at once by adopting the exemplar based model approach. The effectiveness of EBCM is confirmed by experiments on synthetic and real world dat sets.
APA
Kohjima, M., Matsubayashi, T. & Toda, H.. (2019). Exemplar Based Mixture Models with Censored Data. Proceedings of The Eleventh Asian Conference on Machine Learning, in Proceedings of Machine Learning Research 101:535-550 Available from https://proceedings.mlr.press/v101/kohjima19a.html.

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