A Subspace Learning Algorithm For Microwave Scattering Signal Classification With Application To Wood Quality Assessment
Paper in proceedings, 2012

A classification algorithm based on a linear subspace model has been developed and is presented in this paper. To further improve the classification results, the full linear subspace of each class is split into subspaces with lower dimensions and characterized by local coordinates constructed from automatically selected training data. The training data selection is implemented by optimizations with least squares constraints or L1 regularization. The working application is to determine the quality in wooden logs using microwave signals [1]. The experimental results are shown and compared with classical methods

Author

Yinan Yu

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

Tomas McKelvey

Chalmers, Signals and Systems, Signalbehandling och medicinsk teknik, Signal Processing

IEEE International Workshop on Machine Learning for Signal Processing, MLSP

21610363 (ISSN) 21610371 (eISSN)

6349728

Subject Categories

Signal Processing

DOI

10.1109/MLSP.2012.6349728

ISBN

978-146731026-0

More information

Created

10/7/2017