Finite Mixture Model of Nonparametric Density Estimation using Sampling Importance Resampling for Persistence Landscape
Machine Learning
2018-11-21 v1 Machine Learning
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.
Cite
@article{arxiv.1811.08297,
title = {Finite Mixture Model of Nonparametric Density Estimation using Sampling Importance Resampling for Persistence Landscape},
author = {Farzad Eskandari and Soroush Pakniat},
journal= {arXiv preprint arXiv:1811.08297},
year = {2018}
}
Comments
arXiv admin note: text overlap with arXiv:1803.03677