A Study for effectiveness of Dimensionality Reduction for State-action Pair Prediction –Training set reduction using Tendency–

This paper investigates the effectiveness of reduction of training sets and kernel space for actiondecision using future prediction. Considering a working in a real environment based on future prediction, it’s necessary to know the property of its state and disturbance that will be given by the outside environment. On the other hand, obtaining the property of the disturbance depends on specification for target processor, especially, sensor resolution or processing ability of the processor. Therefore, sampling rate settings will be limited by hardware specification. In contrast, in case of a future prediction using a machine learning, it predicts that based on the tendency that obtained by past training or learning. In this kind of situation, the learning time will be proportionally larger to training data. At worst, the prediction algorithm will be hard to work in real time due to time-complexity. In the proposed method, the possibility of carefully analyzing the algorithm and applying dimensionality reduction techniques in order to accelerate the algorithm has been considered. In particular, we will consider that to reduce the training sets and kernel space based on the recent tendency of disturbance or state using FFT and pattern matching will be focused on. From this standpoint, we will propose the method that to dimensionality reduction dynamically based on the tendency of disturbance.