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In this study, a new distance metric that can be used in classification is proposed and its success is investigated. The classification success is increased by using the special distance metric in Lorentzian space. In order to find the optimum angle in transformation from Euclidean space to Lorentzian space, the decision line from Support Vector Machine is(More)
In this study, we propose a new algorithm which works in Lorentzian space with a similar sense in the k-NN method. We exploit the distance metric of Lorentzian space in classification problem. It is a special metric which may give a zero distance for far points. To take best benefit from structural and other properties of the Lorentzian space, a special(More)
Machine Learning is widely used in many areas because of its effectiveness in real-world problems. To make it more efficient a pre-processing is often applied. In this study, we suggest a preprocessing step for classification problem that is based on special Lorentzian space metric. This step consists of the novel Feature Selection in Lorentzian Space(More)
Nowadays multidimensional data as a part of Big Data are collected in every organization and feature selection is one of the main approaches in terms of processing them with machine learning methods. In this study, firstly, a Feature Selection based on Lorentzian Metric (FSLM) is developed. The proposed method unlike from matrix multiplication in Euclidean(More)
Lorentzian geometry is a subject of mathematics and has famous applications in physics, especially in relativity theory. This geometry has interesting features, e.g. one axis has a negative sign in metric definition (time axis). In this study, we try to apply Lorentzian geometry for feature extraction and dimensionality reduction. We use a Lorentzian(More)
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