Application of PCA and Coherence Measure in Clustering Algorithm

An efficient and quick method based on 2-D Principal Component Analysis (PCA) and coherence measure is introduced. The coordinates are achieved by projecting the high dimensional data to the 2-D space after the principle component space is built and feature extraction is finished at one time. Every principle component is the linear combination of the original variables and is irrelevant to each other. A novel coherence measure is introduced and designed for effectively measuring the coherence of a new specimen of unknown type with the training samples. The spectrum can be classified quickly and exactly by the classifier.