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- W2900897923 abstract "Considering the creation of persistence landscape on a parametrized curve and structure of sampling, there exists a random process for which a finite mixture model of persistence landscape (FMMPL) can provide a better description for a given dataset. In this paper, a nonparametric approach for computing integrated mean of square error (IMSE) in persistence landscape has been presented. As a result, FMMPL is more accurate than the another way. Also, the sampling importance resampling (SIR) has been presented a better description of important landmark from parametrized curve. The result, provides more accuracy and less space complexity than the landmarks selected with simple sampling." @default.
- W2900897923 created "2018-11-29" @default.
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- W2900897923 date "2018-11-17" @default.
- W2900897923 modified "2023-09-26" @default.
- W2900897923 title "Finite Mixture Model of Nonparametric Density Estimation using Sampling Importance Resampling for Persistence Landscape." @default.
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