A pattern classification procedure integrating the multivariate statistical analysis with neural networks

Abstract

A new procedure integrating multivariate statistical analysis with artificial neural networks (ANN) for complex pattern classification is proposed. Firstly, a specially designed statistical analysis algorithm called correlative component analysis (CCA) was used to identify the classification characteristics (CC) from original high-dimensional pattern information. These CC were then used as input data to the ANN for pattern classification. The proposed new procedure not only effectively decreased the dimensionality of original patterns, but also took advantage of the self-learning power of the ANN. Further, a typical example of classifying natural spearmint essence was employed to verify the effectiveness of the new pattern classification method. The study showed that this novel integrated procedure provides better results than those obtained using individual methods separately.