A Subspace Learning Algorithm For Microwave Scattering Signal Classification With Application To Wood Quality Assessment
Paper i proceeding, 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

Författare

Yinan Yu

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

Tomas McKelvey

Chalmers, Signaler och system, Signalbehandling och medicinsk teknik, Signalbehandling

IEEE International Workshop on Machine Learning for Signal Processing, MLSP

21610363 (ISSN) 21610371 (eISSN)

6349728

Ämneskategorier

Signalbehandling

DOI

10.1109/MLSP.2012.6349728

ISBN

978-146731026-0

Mer information

Skapat

2017-10-07