Sparse Sensor Placement Optimization for Classification
| Autor: | Bingni W. Brunton, Steven L. Brunton, J. N. Kutz, Joshua L. Proctor |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Dynamical systems theory
Computer science Applied Mathematics Feature vector 020206 networking & telecommunications Feature selection 02 engineering and technology computer.software_genre 01 natural sciences 010305 fluids & plasmas Set (abstract data type) Identification (information) Compressed sensing 0103 physical sciences 0202 electrical engineering electronic engineering information engineering Combinatorial search Data mining Minification computer |
| Zdroj: | SIAM Journal on Applied Mathematics. 76:2099-2122 |
| ISSN: | 1095-712X 0036-1399 |
| DOI: | 10.1137/15m1036713 |
| Popis: | Choosing a limited set of sensor locations to characterize or classify a high-dimensional system is an important challenge in engineering design. Traditionally, optimizing the sensor locations involves a brute-force, combinatorial search, which is NP-hard and is computationally intractable for even moderately large problems. Using recent advances in sparsity-promoting techniques, we present a novel algorithm to solve this sparse sensor placement optimization for classification (SSPOC) that exploits low-dimensional structure exhibited by many high-dimensional systems. Our approach is inspired by compressed sensing, a framework that reconstructs data from few measurements. If only classification is required, reconstruction can be circumvented and the measurements needed are orders-of-magnitude fewer still. Our algorithm solves an $\ell_1$ minimization to find the fewest nonzero entries of the full measurement vector that exactly reconstruct the discriminant vector in feature space; these entries represent sensor locations that best inform the decision task. We demonstrate the SSPOC algorithm on five classification tasks, using datasets from a diverse set of examples, including physical dynamical systems, image recognition, and microarray cancer identification. Once training identifies sensor locations, data taken at these locations forms a low-dimensional measurement space, and we perform computationally efficient classification with accuracy approaching that of classification using full-state data. The algorithm also works when trained on heavily subsampled data, eliminating the need for unrealistic full-state training data. |
| Databáze: | OpenAIRE |
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