Batch optimization methods in Generalized Matrix Relevance Learning Vector Quantization

Classification methods have been used in a variety of academic and commercial applications such as image analysis, bio-informatics, medicine, etc. One of those methods is Matrix Relevance Learning Vector Quantization (MRLVQ). Original MLVQ minimizes the cost function by Stochastic Gradient Descent based on a randomized sequence of single examples. We investigate several more sophisticated optimization methods which make use of the full cost function, i.e. all examples in every iteration step. And compare performance with the Stochastic Gradient Descent method. Per- formance is evaluated in terms of two example problems: the classification of images in dermatology based on color information and the classification of presence of a benign or malignant tumor of the adrenal.