This paper introduces new similarity classifiers using the Heronian mean, and the generalized Heronian mean operators. We examine the use of these operators at the aggregation step within the similarity classifier. The similarity classifier was earlier studied with other operators, in particular with an arithmetic mean, generalized mean, OWA operators, and many more. The two classifiers here ar...

This chapter details similarity discriminant analysis (SDA), a new framework for similaritybased classification. The two defining characteristics of the SDA classification framework are similarity-based and generative. The classifiers in this framework are similarity-based, because they classify based on the pairwise similarities of data samples, and they are generative, because they build clas...

In this article a classification method is proposed where data is first preprocessed using new nonlinear fuzzy robust principal component analysis (NFRPCA) algorithm to get data into more feasible form. After this preprocessing step the similarity classifier is then used for the actual classification. The procedure was tested for dermatology, hepatitis and liver-disorder data. Results were quit...

Similarity-based clustering and semi-supervised learning methods separate the data into clusters or classes according to the pairwise similarity between the data, and the pairwise similarity is crucial for their performance. In this paper, we propose a novel discriminative similarity learning framework which learns discriminative similarity for either data clustering or semi-supervised learning...

Pattern classification is an important task for many practical systems. Many classifier systems rely on similarity measures to classify unknown patterns. Signal patterns are an interesting class of patterns exhibited in many sensorbased systems. In this paper we present three fuzzy similarity measures that can be used for signal pattern classification. We use the three fuzzy similarity measures...

Pairwise clustering methods partition the data space into clusters by the pairwise similarity between data points. The success of pairwise clustering largely depends on the pairwise similarity function defined over the data points, where kernel similarity is broadly used. In this paper, we present a novel pairwise clustering framework by bridging the gap between clustering and multi-class class...