Incremental Feature Extraction Based on Gaussian Maximum Likelihood

Seongyoun Woo; Chulhee Lee

doi:10.1109/ITC-CSCC.2019.8793458

Incremental Feature Extraction Based on Gaussian Maximum Likelihood

Seongyoun Woo, Chulhee Lee

Department of Electrical and Electronic Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

Linear feature extraction is used to lower data dimensionality using linear projection while minimizing information loss. Most previous algorithms have used the batch mode that process all the available data at once. However, incremental algorithms are also important to rapidly process real-time streaming data. Decision boundary feature extraction (DBFE) is a batch feature extraction method that uses the decision boundaries which a classifier defines. In this paper, an incremental gradient descent DBFE (IGDDBFE) is proposed for Gaussian maximum likelihood classifier. The proposed method showed better performance than other existing incremental feature extraction methods when applied to real-world UCI databases.

Original language	English
Title of host publication	34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728132716
DOIs	https://doi.org/10.1109/ITC-CSCC.2019.8793458
Publication status	Published - 2019 Jun
Event	34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019 - JeJu, Korea, Republic of Duration: 2019 Jun 23 → 2019 Jun 26

Publication series

Name	34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019

Conference

Conference	34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019
Country/Territory	Korea, Republic of
City	JeJu
Period	19/6/23 → 19/6/26

All Science Journal Classification (ASJC) codes

Information Systems
Electrical and Electronic Engineering
Artificial Intelligence
Computer Networks and Communications
Hardware and Architecture

Access to Document

10.1109/ITC-CSCC.2019.8793458

Cite this

Woo, S., & Lee, C. (2019). Incremental Feature Extraction Based on Gaussian Maximum Likelihood. In 34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019 [8793458] (34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ITC-CSCC.2019.8793458

Woo, Seongyoun ; Lee, Chulhee. / Incremental Feature Extraction Based on Gaussian Maximum Likelihood. 34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. (34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019).

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abstract = "Linear feature extraction is used to lower data dimensionality using linear projection while minimizing information loss. Most previous algorithms have used the batch mode that process all the available data at once. However, incremental algorithms are also important to rapidly process real-time streaming data. Decision boundary feature extraction (DBFE) is a batch feature extraction method that uses the decision boundaries which a classifier defines. In this paper, an incremental gradient descent DBFE (IGDDBFE) is proposed for Gaussian maximum likelihood classifier. The proposed method showed better performance than other existing incremental feature extraction methods when applied to real-world UCI databases.",

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year = "2019",

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Woo, S & Lee, C 2019, Incremental Feature Extraction Based on Gaussian Maximum Likelihood. in 34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019., 8793458, 34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019, Institute of Electrical and Electronics Engineers Inc., 34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019, JeJu, Korea, Republic of, 19/6/23. https://doi.org/10.1109/ITC-CSCC.2019.8793458

Incremental Feature Extraction Based on Gaussian Maximum Likelihood. / Woo, Seongyoun; Lee, Chulhee.
34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019. Institute of Electrical and Electronics Engineers Inc., 2019. 8793458 (34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - Linear feature extraction is used to lower data dimensionality using linear projection while minimizing information loss. Most previous algorithms have used the batch mode that process all the available data at once. However, incremental algorithms are also important to rapidly process real-time streaming data. Decision boundary feature extraction (DBFE) is a batch feature extraction method that uses the decision boundaries which a classifier defines. In this paper, an incremental gradient descent DBFE (IGDDBFE) is proposed for Gaussian maximum likelihood classifier. The proposed method showed better performance than other existing incremental feature extraction methods when applied to real-world UCI databases.

AB - Linear feature extraction is used to lower data dimensionality using linear projection while minimizing information loss. Most previous algorithms have used the batch mode that process all the available data at once. However, incremental algorithms are also important to rapidly process real-time streaming data. Decision boundary feature extraction (DBFE) is a batch feature extraction method that uses the decision boundaries which a classifier defines. In this paper, an incremental gradient descent DBFE (IGDDBFE) is proposed for Gaussian maximum likelihood classifier. The proposed method showed better performance than other existing incremental feature extraction methods when applied to real-world UCI databases.

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M3 - Conference contribution

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BT - 34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019

PB - Institute of Electrical and Electronics Engineers Inc.

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Woo S, Lee C. Incremental Feature Extraction Based on Gaussian Maximum Likelihood. In 34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019. Institute of Electrical and Electronics Engineers Inc. 2019. 8793458. (34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019). doi: 10.1109/ITC-CSCC.2019.8793458

Incremental Feature Extraction Based on Gaussian Maximum Likelihood

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