Abstract
In the age of big data, the emitter parameter measurement data is generally characteristic of uncertainty in the form of normallydistributed intervals, enormous size and continuous growth. However, existing interval-valued data analysis methods generally assume a uniform distribution instead and are unable to adapt to the rapid growth of volume. To address the above problems, we have brought forward an incremental distributed weighted class discriminant analysis method on interval-valued emitter parameters. Extensive experiments indicate that our method is able to cope with these new characteristics effectively.
Original language | English |
---|---|
Title of host publication | Knowledge Science, Engineering and Management - 8th International Conference, KSEM 2015, Proceedings |
Editors | Zili Zhang, Songmao Zhang, Zili Zhang, Martin Wirsing, Martin Wirsing, Martin Wirsing, Zili Zhang, Songmao Zhang, Songmao Zhang |
Pages | 619-624 |
Number of pages | 6 |
DOIs | |
State | Published - 2015 |
Event | 8th International Conference on Knowledge Science, Engineering and Management, KSEM 2015 - Chongqing, China Duration: Oct 28 2015 → Oct 30 2015 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
---|---|
Volume | 9403 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 8th International Conference on Knowledge Science, Engineering and Management, KSEM 2015 |
---|---|
Country/Territory | China |
City | Chongqing |
Period | 10/28/15 → 10/30/15 |
Bibliographical note
Publisher Copyright:© Springer International Publishing Switzerland 2015.
Keywords
- Class discriminant analysis
- Distributed computing
- Emitter identification
- Fuzzy pattern mining
- Incremental learning
- Signal processing
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science