Kernel Online System for Fast Principal Component Analysis and its Adaptive Learning

Abstract

An artificial neural system for data compression that sequentially processes linearly nonseparable classes is proposed. The main elements of this system include adjustable radial-basis functions (Epanechnikov’s kernels), an adaptive linear associator learned by a multistep optimal algorithm, and Hebb-Sanger neural network whose nodes are formed by Oja’s neurons. For tuning the modified Oja’s algorithm, additional filtering (in case of noisy data) and tracking (in case of nonstationary data) properties were introduced. The main feature of the proposed system is the ability to work in conditions of significant nonlinearity of the initial data that are sequentially fed to the system and have a non-stationary nature. The effectiveness of the developed approach was confirmed by the experimental results. The proposed kernel online neural system is designed to solve compression and visualization tasks when initial data form linearly nonseparable classes in general problem of Data Stream Mining and Dynamic Data Mining. The main benefit of the proposed approach is high speed and ability to process data whose characteristics are changed in time.