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3d5b5060 | 1 | --- |
c83df166 | 2 | title: Use morpheus package |
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3 | |
4 | output: | |
5 | pdf_document: | |
6 | number_sections: true | |
7 | toc_depth: 1 | |
8 | --- | |
9 | ||
10 | ```{r setup, results="hide", include=FALSE} | |
11 | knitr::opts_chunk$set(echo = TRUE, include = TRUE, | |
12 | cache = TRUE, comment="", cache.lazy = FALSE, | |
13 | out.width = "100%", fig.align = "center") | |
14 | ``` | |
15 | ||
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16 | ## Introduction |
17 | <!--Tell that we try to learn classification parameters in a non-EM way, using algebric manipulations.--> | |
3d5b5060 | 18 | |
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19 | *morpheus* is a contributed R package which attempts to find the parameters of a mixture of logistic classifiers. |
20 | When the data under study come from several groups that have different characteristics, using mixture models is a very popular way to handle heterogeneity. | |
21 | Thus, many algorithms were developed to deal with various mixtures models. Most of them use likelihood methods or Bayesian methods that are likelihood dependent. | |
22 | *flexmix* is an R package which implements these kinds of algorithms. | |
3d5b5060 | 23 | |
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24 | However, one problem of such methods is that they can converge to local maxima, so several starting points must be explored. |
25 | Recently, spectral methods were developed to bypass EM algorithms and they were proved able to recover the directions of the regression parameter | |
c83df166 | 26 | in models with known link function and random covariates (see [XX]). |
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27 | Our package extends such moment methods using least squares to get estimators of the whole parameters (with theoretical garantees, see [XX]). |
28 | Currently it can handle only binary output $-$ which is a common case. | |
3d5b5060 | 29 | |
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30 | ## Model |
31 | ||
32 | ||
3d5b5060 | 33 | |
cff1083b | 34 | TODO: retrouver mon texte initial + article. |
3d5b5060 | 35 | |
cff1083b | 36 | 2) Algorithm (as in article) |
3d5b5060 | 37 | |
cff1083b | 38 | 3) Experiments: show package usage |