EM algorithm for GTM-FS

Dharmesh M. Maniyar; Ian T. Nabney

EM algorithm for GTM-FS

Dharmesh M. Maniyar, Ian T. Nabney

Research output: Other contribution

11 Downloads (Pure)

Abstract

We propose a generative topographic mapping (GTM) based data visualization with simultaneous feature selection (GTM-FS) approach which not only provides a better visualization by modeling irrelevant features ("noise") using a separate shared distribution but also gives a saliency value for each feature which helps the user to assess their significance. This technical report presents a varient of the Expectation-Maximization (EM) algorithm for GTM-FS.

Original language	English
Publisher	Aston University
Publication status	Published - 1 Nov 2005

Keywords

generative topographic mapping, data visualization, simultaneous feature selection, Expectation-Maximization algorithm, GTM-FS

Handle.net

Persistent link

Cite this

@misc{321dbde080924dbf9d362dcf7c90f7e3,

title = "EM algorithm for GTM-FS",

abstract = "We propose a generative topographic mapping (GTM) based data visualization with simultaneous feature selection (GTM-FS) approach which not only provides a better visualization by modeling irrelevant features ({"}noise{"}) using a separate shared distribution but also gives a saliency value for each feature which helps the user to assess their significance. This technical report presents a varient of the Expectation-Maximization (EM) algorithm for GTM-FS.",

keywords = "generative topographic mapping, data visualization, simultaneous feature selection, Expectation-Maximization algorithm, GTM-FS",

author = "Maniyar, \{Dharmesh M.\} and Nabney, \{Ian T.\}",

year = "2005",

month = nov,

day = "1",

language = "English",

publisher = "Aston University",

type = "Other",

}

TY - GEN

T1 - EM algorithm for GTM-FS

AU - Maniyar, Dharmesh M.

AU - Nabney, Ian T.

PY - 2005/11/1

Y1 - 2005/11/1

N2 - We propose a generative topographic mapping (GTM) based data visualization with simultaneous feature selection (GTM-FS) approach which not only provides a better visualization by modeling irrelevant features ("noise") using a separate shared distribution but also gives a saliency value for each feature which helps the user to assess their significance. This technical report presents a varient of the Expectation-Maximization (EM) algorithm for GTM-FS.

AB - We propose a generative topographic mapping (GTM) based data visualization with simultaneous feature selection (GTM-FS) approach which not only provides a better visualization by modeling irrelevant features ("noise") using a separate shared distribution but also gives a saliency value for each feature which helps the user to assess their significance. This technical report presents a varient of the Expectation-Maximization (EM) algorithm for GTM-FS.

KW - generative topographic mapping, data visualization, simultaneous feature selection, Expectation-Maximization algorithm, GTM-FS

M3 - Other contribution

PB - Aston University

ER -

EM algorithm for GTM-FS

Abstract

Keywords

Handle.net

Fingerprint

Cite this