Details
| Original language | English |
|---|---|
| Title of host publication | Pattern Recognition |
| Subtitle of host publication | Joint 34th DAGM and 36th OAGM Symposium, Proceedings |
| Pages | 377-386 |
| Number of pages | 10 |
| Publication status | Published - 2012 |
| Event | Joint 34th Symposium of the German Association for Pattern Recognition, DAGM 2012 and 36th Symposium of the Austrian Association for Pattern Recognition, OAGM 2012 - Graz, Austria Duration: 28 Aug 2012 → 31 Aug 2012 |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 7476 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (electronic) | 1611-3349 |
Abstract
In this paper, we propose to enhance particle-based stochastic optimization methods (SO) by using Principal Component Analysis (PCA) to build an approximation of the cost function in a neighborhood of particles during optimization. Then we use it to shift the samples in the direction of maximum cost change. We provide theoretical basis and experimental results showing that such enhancement improves the performance of existing SO methods significantly. In particular, we demonstrate the usefulness of our method when combined with standard Random Sampling, Simulated Annealing and Particle Filter.
ASJC Scopus subject areas
- Mathematics(all)
- Theoretical Computer Science
- Computer Science(all)
Cite this
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Pattern Recognition: Joint 34th DAGM and 36th OAGM Symposium, Proceedings. 2012. p. 377-386 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7476 LNCS).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - PCA-enhanced stochastic optimization methods
AU - Kuznetsova, Alina
AU - Pons-Moll, Gerard
AU - Rosenhahn, Bodo
PY - 2012
Y1 - 2012
N2 - In this paper, we propose to enhance particle-based stochastic optimization methods (SO) by using Principal Component Analysis (PCA) to build an approximation of the cost function in a neighborhood of particles during optimization. Then we use it to shift the samples in the direction of maximum cost change. We provide theoretical basis and experimental results showing that such enhancement improves the performance of existing SO methods significantly. In particular, we demonstrate the usefulness of our method when combined with standard Random Sampling, Simulated Annealing and Particle Filter.
AB - In this paper, we propose to enhance particle-based stochastic optimization methods (SO) by using Principal Component Analysis (PCA) to build an approximation of the cost function in a neighborhood of particles during optimization. Then we use it to shift the samples in the direction of maximum cost change. We provide theoretical basis and experimental results showing that such enhancement improves the performance of existing SO methods significantly. In particular, we demonstrate the usefulness of our method when combined with standard Random Sampling, Simulated Annealing and Particle Filter.
UR - http://www.scopus.com/inward/record.url?scp=84865832740&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-32717-9_38
DO - 10.1007/978-3-642-32717-9_38
M3 - Conference contribution
AN - SCOPUS:84865832740
SN - 9783642327162
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 377
EP - 386
BT - Pattern Recognition
T2 - Joint 34th Symposium of the German Association for Pattern Recognition, DAGM 2012 and 36th Symposium of the Austrian Association for Pattern Recognition, OAGM 2012
Y2 - 28 August 2012 through 31 August 2012
ER -