Abstract
A coronary plaque tissue classification is essential for diagnosis of acute coronary syndromes. We have applied the Averaged Learning Subspace Method (ALSM) with consideration for the neighborhood information, to classify coronary plaque tissues. We have succeeded in classifying the tissues whilst keeping the merit of the subspace method. Simple parameter settings and low computing cost have been realized, and compared to our previous method more accurate classification results have been obtained.
