Browsing by Author "Taskin, Gulsen"

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Citation - Scopus: 3
Feature Selection Using Self Organizing Map Oriented Evolutionary Approach
(Ieee, 2021) Ceylan, Oguzhan; Ceylan, Oğuzhan; Taskin, Gulsen
Hyperspectral images are the multidimensional matrices consisting of hundreds of spectral feature vectors. Thanks to these large number of features, the objects on the Earth having similar spectral characteristics can easily be distinguished from each other. However, the high correlation and the noise between these features cause a significant decrease in the classification performances, especially in the supervised classification tasks. In order to overcome these problems, which is known in the literature as Hughes's effects or curse of dimensionality, dimensionality reduction techniques have frequently been used. Feature selection and feature extraction methods are the ones used for this purpose. The feature selection methods aim to remove the features, including high correlation and noise, out of the original feature set. In other words, a subset of relevant features that have the ability to distinguish the objects is determined. The feature extraction methods project the high dimensional space into a lower-dimensional feature space based on some optimization criterion, and hence they distort the original characteristic of the dataset. Therefore, the feature selection methods are more preferred than the feature extraction methods since they preserve the originality of the dataset. Based on this motivation, an evolutionary based optimization algorithm utilizing self organizing map was accordingly modified to provide a new feature selection method for the classification of hyperspectral images. The proposed method was compared to well-known feature selection methods in the classification of two hyperspectral datasets: Botswana and Indian Pines. According to the preliminary results, the proposed method achieves higher performance over other feature selection methods with a very less number of features.
Citation - WoS: 2
Citation - Scopus: 3
A Scalable Unsupervised Feature Selection With Orthogonal Graph Representation for Hyperspectral Images
(IEEE-Inst Electrical Electronics Engineers Inc, 2023) Yetkin, Emrullah Fatih; Yetkin, E. Fatih; Camps-Valls, Gustau
Feature selection (FS) is essential in various fields of science and engineering, from remote sensing to computer vision. Reducing data dimensionality by removing redundant features and selecting the most informative ones improves machine learning algorithms' performance, especially in supervised classification tasks, while lowering storage needs. Graph-embedding (GE) techniques have recently been found efficient for FS since they preserve the geometric structure of the original feature space while embedding data into a low-dimensional subspace. However, the main drawback is the high computational cost of solving an eigenvalue decomposition problem, especially for large-scale problems. This article addresses this issue by combining the GE framework and representation theory for a novel FS method. Inspired by the high-dimensional model representation (HDMR), the feature transformation is assumed to be a linear combination of a set of univariate orthogonal functions carried out in the GE framework. As a result, an explicit embedding function is created, which can be utilized to embed out-of-samples into low-dimensional space and provide a feature relevance score. The significant contribution of the proposed method is to divide an $n$ -dimensional generalized eigenvalue problem into $n$ small-sized eigenvalue problems. With this property, the computational complexity (CC) of the GE is significantly reduced, resulting in a scalable FS method, which could be easily parallelized too. The performance of the proposed method is compared favorably to its counterparts in high-dimensional hyperspectral image (HSI) processing in terms of classification accuracy, feature stability, and computational time.