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Algorithms for Similarity Relation Learning from High Dimensional Data

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Transactions on Rough Sets XVII

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 8375))

Abstract

The notion of similarity plays an important role in machine learning and artificial intelligence. It is widely used in tasks related to a supervised classification, clustering, an outlier detection and planning. Moreover, in domains such as information retrieval or case-based reasoning, the concept of similarity is essential as it is used at every phase of the reasoning cycle. The similarity itself, however, is a very complex concept that slips out from formal definitions. A similarity of two objects can be different depending on a considered context. In many practical situations it is difficult even to evaluate the quality of similarity assessments without considering the task for which they were performed. Due to this fact the similarity should be learnt from data, specifically for the task at hand. This paper presents a research on the problem of similarity learning, which is a part of author’s PHD dissertation. It describes a similarity model, called Rule-Based Similarity, and shows algorithms for constructing this model from available data. The model utilizes notions from the rough set theory to derive a similarity function that allows to approximate the similarity relation in a given context. It is largely inspired by the idea of Tversky’s feature contrast model and it has several analogical properties. In the paper, those theoretical properties are described and discussed. Moreover, the paper presents results of experiments on real-life data sets, in which a quality of the proposed model is thoroughly evaluated and compared with the state-of-the-art algorithms.

The research was supported by the grants DEC-2011/01/B/ST6/03867 and DEC-2012/05/B/ST6/03215 from the National Research Centre, and the National Centre for Research and Development (NCBiR) under the grant SP/I/1/77065/10 by the strategic scientific research and experimental development program: “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.

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  1. Faculty of Mathematics, Informatics, and Mechanics, The University of Warsaw, Banacha 2, 02-097, Warszawa, Poland

    Andrzej Janusz

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  1. Computational Intelligence Laboratory, ECE Department, University of Manitoba, MB R3T 5V6, Winnipeg, Canada

    James F. Peters

  2. Institute of Mathematics, University of Warsaw, Banacha 2, 02-097, Warsaw, Poland

    Andrzej Skowron

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Janusz, A. (2014). Algorithms for Similarity Relation Learning from High Dimensional Data. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets XVII. Lecture Notes in Computer Science, vol 8375. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54756-0_7

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