ABSTRACT
In this paper, core flow, laminar flow, circulation flow and trickle flow are selected to be tested.
The first key step before training and recognition is feature extraction. Linear Discriminate Analysis is employed in this paper. LDA method is widely used in pattern recognition and machine learning to find a linear combination of features which characterizes or separates two or more classes of objects or events[2]. The resulting combination may be used as a linear classifier, or more commonly for dimensionality reduction before later classification. LDA is also closely related to Principle Component Analysis (PCA) and factor analysis in that they both look for linear combinations of variables which beat explain the data[3] LDA attempts to model the difference between the classes of data. PCA on the other hand does not take into account any difference in class, and factor analysis builds the feature combinations based on differences rather than similarities.
