WoS İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12416/8653
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Conference Object Covariance Features for Trajectory Analysis(IEEE, 2016) Karadeniz, Talha; Maras, Hadi HakanIn this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results.Conference Object Covariance Features for Trajectory Analysis(Institute of Electrical and Electronics Engineers Inc., 2016) Karadeniz, T.; Maras, H.H.In this work, we aimed to demonstrate that covariance estimation methods can be used for trajectory classification. We have shown that, features obtained via shrunk covariance estimation are suitable for describing trajectories. We have arrived to the conclusion that, when compared to Dynamic Time Warping, the explained technique is faster and may yield more accurate results. © 2017 Elsevier B.V., All rights reserved.Conference Object Evaluation of Clustering Performance of Hyperspectral Bands(Ieee, 2015) Sakarya, Ufuk; Toreyin, Behcet Ugur; Haliloglu, Onur; Haliloʇlu, OnurHyperspectral images have huge data volume that contains spectral and spatial information. This high data volume leads to processing, storage, and transmission problems. Moreover, insufficient training data results in Hughes phenomenon. It is possible to solve these problems with the help of feature selection. In this paper, a method that evaluates the clustering performance of spectral bands is proposed as a pre-processing operation in order to realize feature selection. This method is clustering each spectral band based on "dominant sets" technique and it evaluates the clustering performance of each band. The proposed method is time efficient since it works on a small set of training data instead of the whole hyperspectral data. In this study, "dominant sets" technique is first applied to hyperspectral image processing as a clustering method.Conference Object Citation - WoS: 2Citation - Scopus: 3Classification of Fmri Data by Using Clustering(Ieee, 2015) Mogultay, Hazal; Alkan, Sarper; Yarman-Vural, Fatos T.; Moʇultay, HazalRecognition of the the cognitive states by using functional Magnetic Rezonans Imaging (fMRI) data is a challenging problem that has been a focus of scientific research for a long time. In this study the effectiveness of clustering and the ensemble learning techniques on fMRI dataset is investigated and different paramaters are compared. Moreover, the performance of these techniques are tested on both raw voxel intensity values and meshes formed by multiple voxels. Clusters are compared to the functional brain regions, however higher performances are obtained when the number of clusters is higher than the number of functional brain regions.