Next Article in Journal
Sampling Effects on Algorithm Selection for Continuous Black-Box Optimization
Previous Article in Journal
Mobile-Aware Deep Learning Algorithms for Malaria Parasites and White Blood Cells Localization in Thick Blood Smears
Previous Article in Special Issue
Segment-Based Clustering of Hyperspectral Images Using Tree-Based Data Partitioning Structures
Open AccessArticle

Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA

Centre for Intelligent Systems and School of Engineering and Technology, Central Queensland University, Rockhampton, QLD 4701, Australia
*
Author to whom correspondence should be addressed.
Received: 3 December 2020 / Revised: 5 January 2021 / Accepted: 7 January 2021 / Published: 11 January 2021
(This article belongs to the Special Issue Algorithms in Hyperspectral Data Analysis)
Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system. View Full-Text
Keywords: extreme learning machine; spectral data analysis; PCA; backscattering spectra extreme learning machine; spectral data analysis; PCA; backscattering spectra
Show Figures

Figure 1

MDPI and ACS Style

Li, M.; Wibowo, S.; Li, W.; Li, L.D. Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA. Algorithms 2021, 14, 18. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010018

AMA Style

Li M, Wibowo S, Li W, Li LD. Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA. Algorithms. 2021; 14(1):18. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010018

Chicago/Turabian Style

Li, Michael; Wibowo, Santoso; Li, Wei; Li, Lily D. 2021. "Quantitative Spectral Data Analysis Using Extreme Learning Machines Algorithm Incorporated with PCA" Algorithms 14, no. 1: 18. https://0-doi-org.brum.beds.ac.uk/10.3390/a14010018

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop