METHODS AND CHARACTERISTICS OF LOCALITY-PRESERVING TRANSFORMATIONS IN THE PROBLEMS OF COMPUTATIONAL INTELLIGENCE

S. A. Subbotin

Abstract


The problem of the development of mathematical support for data dimensionality reduction is solved. Its results can be used to automate the process of diagnostic and recognizing model construction by precedents. The set of rapid transformations from the original multidimensional space to the one-dimensional axis was firstly proposed. They provide a solution of the feature extraction and feature selection problems. The complex of indicators characterizing the properties of transformations was firstly proposed. On the basis of the proposed indicators the set of criteria was defined. It facilitate comparison and selection of the best transformations and results of their work in diagnosis and recognition problems solving on the basis of computational intelligence methods. The software realizing proposed transformations and indicators characterizing their properties was developed. The experimental study of proposed transformations and indicators was conducted, which results allow to recommend the proposed transformations for use in practice.

Keywords


sample, instance, feature, locality-preserving transformation, hashing, pattern recognition, diagnosis, dimensionality reduction.

References


Jensen, R. Computational intelligence and feature selection: rough and fuzzy approaches / R. Jensen, Q. Shen. – Hoboken: John Wiley & Sons, 2008. – 339 p.

Бабак, О. В. Решение некоторых задач обработки данных на основе метода генеральной обобщенной переменной / О. В. Бабак // Проблемы управления и информатики. – 2002. – № 6. – С. 79–91.

Интеллектуальные информационные технологии проектирования автоматизированных систем диагностирования и распознавания образов : монография / [С. А. Субботин, Ан. А. Олейник, Е. А. Гофман и др.] ; под ред. С. А. Субботина. – Харьков : Компания СМИТ, 2012. – 318 с.

Lee, T. W. Independent component analysis: theory and applications / T. W. Lee. – Berlin: Springer, 2010. – 248 p.

Lee, J. A. Nonlinear dimensionality reduction / J. A. Lee, M. Verleysen. – New York : Springer, 2007. – 308 p.

Dimension reduction : technical report UCD-CSI-2007-7 / University College Dublin ; C. Padraig. – Dublin, 2007. – 24 p.

Multifactor dimensionality reduction for detecting haplotypehaplotype interaction / Y. Jiang, R. Zhang, G. Liu [et al.] // Fuzzy systems and knowledge discovery : Sixth international conference, Tianjin, 14–16 August 2009 : proceedings. – Los Alamitos: IEEE, 2009. – P. 241 –245.

Kulis, B. Fast low-rank semidefinite programming for embedding and clustering [Electronic resource] / B. Kulis, A. C. Surendran, J. C. Platt // Artificial intelligence and statistics : Eleventh international conference, San Juan, 21–24 March 2007 : proceedings / eds.: M. Meila, X. Shen. – Madison: Omnipress, 2007. – 8 p.

Бабак, О. В. Об одном подходе к решению задач классификации в условиях неполноты информации / О. В. Бабак, А. Э. Татаринов // Кибернетика и системный анализ. – 2005. – № 6. – С. 116–123.

Васильев, В. И. Принцип редукции в задачах обнаружения закономерностей : монография / В. И. Васильев, А. И. Шевченко, С. Н. Эш. – Донецк : Наука і освіта, 2009. – 340 с.

Yu, S. Feature selection and classifier ensembles: a study on hyperspectral remote sensing data : proefschrift ... doctor in de wetenschappen / Yu Shixin. – Antwerpen : Universitaire Instelling Antwerpen, 2003. – 124 p.

Super-bit locality-sensitive hashing / [J. Jianqiu, J. Li, Sh. Yany, B. Zhang et al.] // Advances in Neural Information Processing Systems / [eds. P. Bartlett et al.]. – 2012. – Vol. 25. – P. 108–116.

Andoni, A. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions / A. Andoni, P. Indyk // Communications of the ACM. – 2008. – Vol. 51, No. 1. – P. 117–122.

Yang, X. A scalable index architecture for supporting multidimensional range queries in peer-to-peer networks / X. Yang and Y. Hu // Collaborative computing: networking, applications and worksharing : International conference CollaborateCom-2006, Atlanta 17–20 November 2006 : proceedings. – P. 1–10.

Locality-sensitive hashing scheme based on p-stable distributions / A. Andoni, M. Datar, N. Immorlica, P. Indyk, V. Mirrokni // Nearest neighbor methods in learning and vision: theory and practice / [eds.: T. Darrell, P. Indyk, G. Shakhnarovich]. – MIT Press, 2006. – P. 55–67.


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DOI: https://doi.org/10.15588/1607-3274-2014-1-17



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