Priority-based Soft Vector Quantization Feature Maps

Vector quantization techniques using self-organizing maps (SOM) and its variants are popularly used for applications in contextual data clustering, data visualization and high-dimensional data exploration. The update rule in a SOM is based on competitive learning using a neighborhood function that measures the Euclidean distance between an input vector and a non-linear processing element without any consideration of selective priority for a specific feature in the input data. Certain applications may require unsupervised learning of high-dimensional data with a priori knowledge on the priority of certain features, leading to resolution dependent contextual maps. With this in mind, we propose a vector quantization technique called priority based soft vector quantization feature maps (PSVQFM) for creating contextual feature maps by learning priorities. In this paper, we formulate the cost function based on the priorities over the feature coordinates and derive a learning rule from first principles. We present an analysis on the misclassification error and prove that the proposed algorithm is asymptotically optimal. Simulation results over a synthetic data set and a high-dimensional banking corpus data set show that the PSVQFM algorithm is able to learn the input data based on priority of features very well.