ISSN:
0886-9383
Keywords:
Pattern recognition
;
SIMCA
;
PCA
;
Classification
;
Recall
;
Cross-validation
;
Indicator function
;
Leave-x-out
;
Pseudo random data sets
;
Chemistry
;
Analytical Chemistry and Spectroscopy
Source:
Wiley InterScience Backfile Collection 1832-2000
Topics:
Chemistry and Pharmacology
Notes:
The SIMCA pattern recognition method has been evaluated with pseudo random data sets. The number of objects varied from 5 to 50 and the number of features from 5 to 300.First, the determination of the significant number of PCs in the SIMCA models by the cross-validation method was compared with the indicator function. The results showed that for the lower dimensions (≤ 15 objects or ≤ 15 features) the indicator function produces more reliable results.Second, the classification results with SIMCA were analysed for data sets with two equally sized classes and a varying number of objects and features, using the recall function as the evaluation criterion. The results showed that the SIMCA classifier produces reliable results at the first classification level, even for a low object/feature ratio (5/300). However, at the second level the classification performance of SIMCA decreases rapidly with an increasing number of features, even when the data set consists of two very well separated classes and little random error.
Additional Material:
6 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/cem.1180010404
