A Survey on Dimensionality Reduction Techniques for Time-Series Data

Author:

Ashraf Mohsena1,Anowar Farzana2ORCID,Setu Jahanggir H.3,Chowdhury Atiqul I.4,Ahmed Eshtiak5,Islam Ashraful6ORCID,Al-Mamun Abdullah7

Affiliation:

1. Department of Computer Science, University of Colorado Boulder, Boulder, CO, USA

2. Department of Computer Science, University of Regina, Regina, SK, Canada

3. Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh

4. Department of Computer Science and Engineering, United International University, Dhaka, Bangladesh

5. Faculty of Information Technology and Communication Sciences, Tampere University, Tampere, Finland

6. Center for Computational and Data Sciences (CCDS), Independent University, Bangladesh, Dhaka, Bangladesh

7. School of Computer and Cyber Sciences, Augusta University, Augusta, GA, USA

Funder

Independent University, Bangladesh

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Reference140 articles.

1. Kernel-based methods and function approximation

2. Kernel PCA for novelty detection

3. Kernel principal component analysis and its applications in face recognition and active shape models;wang;arXiv 1207 3538,2012

4. Enhanced statistical analysis of nonlinear processes using KPCA, KICA and SVM

5. Performance analysis of dimensionality reduction using PCA, KPCA and LLE for ECG signals;mita;IOP Conf Ser Mater Sci Eng,2021

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