Streaming data classification method using scalable hyper-ellipsoids with linear discriminant projection distance ratio

Abstract

Learning streaming data with limited size of memory storage becomes an interesting problem. Although there have been several learning methods recently proposed, based on the interesting concept of discard-after-learn, the performance of these issues: the learning speed, number of redundant neurons, and classification accuracy of these methods can be further improved in terms of faster speed, less number of neurons, and higher accuracy. The following new four concepts and approaches were proposed in this dissertation: (1) a more generic structure of hyper-ellipsoidal function called Scalable Hyper-Ellipsoidal Function (SHEF) capable of handling the problem of curse of dimensionality by introducing a regularization parameter into the covariance matrix of SHEF; (2) a new recursive function to update the covariance matrix of SHEF based on only the incoming data chunk; (3) a fast and easy conditions to test the states of being overlapped, inside, or touch of two SHEFs; (4) a new distance measure for determining the class of a queried datum based on the projected distance on LDA discriminant vector. The experimental results show the significant improvement when compared with other methods.