Accepted Paper: Exemplar Based Mixture Models with Censored Data

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Masahiro Kohjima (NTT Corporation); Tatsushi Matsubayashi (NTT Corporation); Hiroyuki Toda (NTT)


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 data sets.