3rd International Conference on Automation, Control, Engineering and Computer Science (ACECS-2016) 20 - 22 March 2016 - Hammamet, Tunisia Dimension Reduction Based on Scattering Matrices and Classification Using Fisher's Linear Discriminant Nasar Aldian Ambark Shashoa#1, Salah Mohamed Naas #2, Abdurrezag S. Elmezughi #2 #1 Electrical and Electronics Engineering Department, Azzaytuna University #2 Computer Engineering Department, Azzaytuna University Tarhuna, Libya
[email protected] [email protected] [email protected] Abstract— This paper presents Feature Extraction Based on Y , the detection method generate parameter estimatesˆ , Scattering Matrices for Classification using fisher's linear which are called features[3]. Although all these techniques are discriminant approach. The recursive least square identification algorithm is estimated for autoregressive model (ARX) system. well designed for the fault detection, one of the most relevant Next, Dimension reduction based on scattering matrices is used techniques for the diagnosis is the supervised classification. It to obtain good classification performance. The classification is not unusual to see the fault diagnosis as a classification task between two classes is done by a fisher's linear discriminant. Our whose objective is to classify new observations to one of the simulation results illustrate the usefulness of the proposed existing classes. Many methods have been developed for procedures. supervised classification. Such as the Fisher Discriminant. Nevertheless, the classification (diagnosis) is a hard task in a Keywords— Scattering Matrices; Fault Diagnosis; Feature large number of parameters. Indeed, it is not uncommon for a Selection; Fisher's linear discriminant. process to be described by a large number of parameters, I. INTRODUCTION where not all parameters are of equal informative value, such that it is possible to describe the behavior of the process well Feature selection is a process of selecting a small number of enough using a smaller set of parameters.