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Data Exploration by Representative Region Selection : Axioms and Convergence

We present a new type of unsupervised learning problem in which we find a small set of representative regions that approximates a larger data set. These regions may be presented to a practitioner along with additional information in order to help the practitioner explore the data set. An advantage of this approach is that it does not rely on cluster structure of the data. We formally define this problem, and we present axioms that should be satisfied by functions that measure the quality of representatives. We provide a quality function that satisfies all of these axioms. Using this quality function, we formulate two optimization problems for finding representatives. We provide convergence results for a general class of methods, and we show that these results apply to several specific methods, including methods derived from the solution of the optimization problems formulated in this paper. We provide an example of how representative regions may be used to explore a data set.

Ketersediaan

Informasi Detail

Judul Seri

MATHEMATICS OF OPERATIONS RESEARCH; Vol.46 No.3 August 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 970 - 1007

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

DATA ANALYSIS
DENSITY ESTIMATION
UNSUPERVISED LEARNING
REPRESENTATIVE REGION SELECTION
DATA SUMMARIZATION

Info Detail Spesifik

https://doi.org/10.1287/moor.2020.1115

Pernyataan Tanggungjawab

Alexander S. Estes, Michael O. Ball, David J. Lovell

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