Pattern recognition based on specific weights. (2018) - Singh, Saurabh; Sinha, Madhavi
Feature weighting has attracted lots of researchers in pattern classification and data mining community. To select an appropriate subset of features, original feature set is first weighted according to its importance in determining its class label, and then using a threshold, a subset of features is obtained. Some adequate research works have been reported, which used feature weights for clustering by k-means algorithm. Few such algorithms have been developed, but as far as best of our knowledge, weighted pattern vectors have not been used by any pattern recognition algorithm especially in biometrics. In this paper, we formulate a unique pattern specific feature based weighting method, which weights the features of an individual pattern vector on the basis of their discriminative power to recognise that pattern. Proposed approach yields FAR = 0% at FRR = 0.2 for Euclidian distance and 0.02% and 0.3 respectively for cosine similarity measure.