Modeling text with generalizable Gaussian mixtures

Lars Kai Hansen; Sigurdur Sigurdsson; Thomas Kolenda; Finn Årup Nielsen; Ulrik Kjems; Jan Larsen

doi:10.1109/ICASSP.2000.860154

Modeling text with generalizable Gaussian mixtures

Lars Kai Hansen, Sigurdur Sigurdsson, Thomas Kolenda, Finn Årup Nielsen, Ulrik Kjems, Jan Larsen

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Abstract

We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss the relation between supervised and unsupervised learning in the test data. Finally, we implement a novelty detector based on the density model.

Original language	English
Title of host publication	IEEE Procedings of Acoustics, Speech, and Signal Processing
Volume	6
Place of Publication	Istanbul, Turkey
Publisher	IEEE
Publication date	2000
Pages	3494-3497
ISBN (Print)	0-7803-6293-4
DOIs	https://doi.org/10.1109/ICASSP.2000.860154
Publication status	Published - 2000
Event	1995 IEEE International Conference on Acoustics, Speech, and Signal Processing - Detroit, United States Duration: 8 May 1995 → 12 May 1995 Conference number: 20

Conference

Conference	1995 IEEE International Conference on Acoustics, Speech, and Signal Processing
Number	20
Country/Territory	United States
City	Detroit
Period	08/05/1995 → 12/05/1995

Bibliographical note

Copyright: 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE

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@inproceedings{b35c7b94a85a4f108cddd965f7e3e3ea,

title = "Modeling text with generalizable Gaussian mixtures",

abstract = "We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss the relation between supervised and unsupervised learning in the test data. Finally, we implement a novelty detector based on the density model.",

author = "Hansen, {Lars Kai} and Sigurdur Sigurdsson and Thomas Kolenda and Nielsen, {Finn {\AA}rup} and Ulrik Kjems and Jan Larsen",

note = "Copyright: 2000 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE; 1995 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '95 ; Conference date: 08-05-1995 Through 12-05-1995",

year = "2000",

doi = "10.1109/ICASSP.2000.860154",

language = "English",

isbn = "0-7803-6293-4",

volume = "6",

pages = "3494--3497",

booktitle = "IEEE Procedings of Acoustics, Speech, and Signal Processing",

publisher = "IEEE",

address = "United States",

}

Hansen, LK, Sigurdsson, S, Kolenda, T, Nielsen, FÅ, Kjems, U & Larsen, J 2000, Modeling text with generalizable Gaussian mixtures. in IEEE Procedings of Acoustics, Speech, and Signal Processing. vol. 6, IEEE, Istanbul, Turkey, pp. 3494-3497, 1995 IEEE International Conference on Acoustics, Speech, and Signal Processing, Detroit, Michigan, United States, 08/05/1995. https://doi.org/10.1109/ICASSP.2000.860154

TY - GEN

T1 - Modeling text with generalizable Gaussian mixtures

AU - Hansen, Lars Kai

AU - Sigurdsson, Sigurdur

AU - Kolenda, Thomas

AU - Nielsen, Finn Årup

AU - Kjems, Ulrik

AU - Larsen, Jan

N1 - Conference code: 20

PY - 2000

Y1 - 2000

N2 - We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss the relation between supervised and unsupervised learning in the test data. Finally, we implement a novelty detector based on the density model.

AB - We apply and discuss generalizable Gaussian mixture (GGM) models for text mining. The model automatically adapts model complexity for a given text representation. We show that the generalizability of these models depends on the dimensionality of the representation and the sample size. We discuss the relation between supervised and unsupervised learning in the test data. Finally, we implement a novelty detector based on the density model.

U2 - 10.1109/ICASSP.2000.860154

DO - 10.1109/ICASSP.2000.860154

M3 - Article in proceedings

SN - 0-7803-6293-4

VL - 6

SP - 3494

EP - 3497

BT - IEEE Procedings of Acoustics, Speech, and Signal Processing

PB - IEEE

CY - Istanbul, Turkey

T2 - 1995 IEEE International Conference on Acoustics, Speech, and Signal Processing

Y2 - 8 May 1995 through 12 May 1995

ER -

Modeling text with generalizable Gaussian mixtures

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