Dimensionality reduction and (bucket) ranking: a mass transportation approach
Whereas most dimensionality reduction techniques (\textit {eg} PCA, ICA, NMF) for
multivariate data essentially rely on linear algebra to a certain extent, summarizing ranking
data, viewed as realizations of a random permutation $\Sigma $ on a set of items indexed by
$ i\in\{1,\ldots,;{n}\} $, is a great statistical challenge, due to the absence of vector space
structure for the set of permutations $\mathfrak {S} _n $. It is the goal of this article to develop
an original framework for possibly reducing the number of parameters required to describe …
multivariate data essentially rely on linear algebra to a certain extent, summarizing ranking
data, viewed as realizations of a random permutation $\Sigma $ on a set of items indexed by
$ i\in\{1,\ldots,;{n}\} $, is a great statistical challenge, due to the absence of vector space
structure for the set of permutations $\mathfrak {S} _n $. It is the goal of this article to develop
an original framework for possibly reducing the number of parameters required to describe …
[引用][C] Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach.
S Clémençon, M Achab, A Korba - 2019 - telecom-paris.hal.science
Dimensionality Reduction and (Bucket) Ranking: a Mass Transportation Approach. -
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