La Trobe University
Data-structure preserved visualization of high-dimensional data reveals the dataset borders and the spread and overlapping tendency of the class borders in a more informative manner than the usual data-topology preserved mapping produced by Self-Organizing Maps (SOMs). Hence, an extension of SOM called Probabilistic Regularized SOM (PRSOM) is proposed for the data-structure preservation in the visualization; however, PRSOM is less suitable for the classification task due to its regularized positioning of the prototypes. In many practical applications, a good classification rate and data-structure informative visualization of high-dimensional data is simultaneously required from an employed method. However, it is difficult to find a method in the cur-rent literature that can perform these two tasks effectively.