scholarly journals A Review of Dimensionality Reduction Techniques for Processing Hyper-Spectral Optical Signal

2019 ◽  
pp. 85-98 ◽  
Author(s):  
Ana del Águila ◽  
Dmitry S. Efremenko ◽  
Thomas Trautmann
Keyword(s):  
Remote Sensing ◽  
Dimensionality Reduction ◽  
Principal Component ◽  
Optical Signal ◽  
Spectral Radiance ◽  
Electromagnetic Spectrum ◽  
Spectral Processing ◽  
Reduction Techniques ◽  
Hyper Spectral ◽  
Dimensionality Reduction Techniques

Hyper-spectral sensors take measurements in the narrow contiguous bands across the electromagnetic spectrum. Usually, the goal is to detect a certain object or a component of the medium with unique spectral signatures. In particular, the hyper-spectral measurements are used in atmospheric remote sensing to detect trace gases. To improve the efficiency of hyper-spectral processing algorithms, data reduction methods are applied. This paper outlines the dimensionality reduction techniques in the context of hyper-spectral remote sensing of the atmosphere. The dimensionality reduction excludes redundant information from the data and currently is the integral part of high-performance radiation transfer models. In this survey, it is shown how the principal component analysis can be applied for spectral radiance modelling and retrieval of atmospheric constituents, thereby speeding up the data processing by orders of magnitude. The discussed techniques are generic and can be readily applied for solving atmospheric as well as material science problems.

scholarly journals A Spectral Acceleration Approach for the Spherical Harmonics Discrete Ordinate Method

2020 ◽  
Vol 12 (22) ◽  
pp. 3703
Author(s):  
Adrian Doicu ◽  
Dmitry S. Efremenko ◽  
Thomas Trautmann
Keyword(s):  
Dimensionality Reduction ◽  
Spherical Harmonics ◽  
Principal Component ◽  
Optical Parameters ◽  
Discrete Ordinate Method ◽  
Spectral Acceleration ◽  
Discrete Ordinate ◽  
Distribution Method ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

A spectral acceleration approach for the spherical harmonics discrete ordinate method (SHDOM) is designed. This approach combines the correlated k-distribution method and some dimensionality reduction techniques applied on the optical parameters of an atmospheric system. The dimensionality reduction techniques used in this study are the linear embedding methods: principal component analysis, locality pursuit embedding, locality preserving projection, and locally embedded analysis. Through a numerical analysis, it is shown that relative to the correlated k-distribution method, PCA in conjunction with a second-order of scattering approximation yields an acceleration factor of 12. This implies that SHDOM equipped with this acceleration approach is efficient enough to perform spectral integration of radiance fields in inhomogeneous multi-dimensional media.

scholarly journals A SURVEY ON THE CURES FOR THE CURSE OF DIMENSIONALITY IN BIG DATA

2017 ◽  
Vol 10 (13) ◽  
pp. 355 ◽  
Author(s):  
Reshma Remesh ◽  
Pattabiraman. V
Keyword(s):  
Dimensionality Reduction ◽  
Input Data ◽  
Principal Component ◽  
Kernel Principal Component Analysis ◽  
High Dimensional ◽  
Data Sets ◽  
Learning Approaches ◽  
Data Set ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

Dimensionality reduction techniques are used to reduce the complexity for analysis of high dimensional data sets. The raw input data set may have large dimensions and it might consume time and lead to wrong predictions if unnecessary data attributes are been considered for analysis. So using dimensionality reduction techniques one can reduce the dimensions of input data towards accurate prediction with less cost. In this paper the different machine learning approaches used for dimensionality reductions such as PCA, SVD, LDA, Kernel Principal Component Analysis and Artificial Neural Network  have been studied.

scholarly journals Integration of Selective Dimensionality Reduction Techniques for Mineral Exploration Using ASTER Satellite Data

2020 ◽  
Vol 12 (8) ◽  
pp. 1261 ◽  
Author(s):  
Hodjat Shirmard ◽  
Ehsan Farahbakhsh ◽  
Amin Beiranvand Pour ◽  
Aidy M Muslim ◽  
R. Dietmar Müller ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Dimensionality Reduction ◽  
Hydrothermal Alteration ◽  
Mineral Exploration ◽  
Transformation Techniques ◽  
Thematic Layers ◽  
Alteration Zones ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Hydrothermal Alteration Zones

There are a significant number of image processing methods that have been developed during the past decades for detecting anomalous areas, such as hydrothermal alteration zones, using satellite images. Among these methods, dimensionality reduction or transformation techniques are known to be a robust type of methods, which are helpful, as they reduce the extent of a study area at the initial stage of mineral exploration. Principal component analysis (PCA), independent component analysis (ICA), and minimum noise fraction (MNF) are the dimensionality reduction techniques known as multivariate statistical methods that convert a set of observed and correlated input variables into uncorrelated or independent components. In this study, these techniques were comprehensively compared and integrated, to show how they could be jointly applied in remote sensing data analysis for mapping hydrothermal alteration zones associated with epithermal Cu–Au deposits in the Toroud-Chahshirin range, Central Iran. These techniques were applied on specific subsets of the advanced spaceborne thermal emission and reflection radiometer (ASTER) spectral bands for mapping gossans and hydrothermal alteration zones, such as argillic, propylitic, and phyllic zones. The fuzzy logic model was used for integrating the most rational thematic layers derived from the transformation techniques, which led to an efficient remote sensing evidential layer for mineral prospectivity mapping. The results showed that ICA was a more robust technique for generating hydrothermal alteration thematic layers, compared to the other dimensionality reduction techniques. The capabilities of this technique in separating source signals from noise led to improved enhancement of geological features, such as specific alteration zones. In this investigation, several previously unmapped prospective zones were detected using the integrated hydrothermal alteration map and most of the known hydrothermal mineral occurrences showed a high prospectivity value. Fieldwork and laboratory analysis were conducted to validate the results and to verify new prospective zones in the study area, which indicated a good consistency with the remote sensing output. This study demonstrated that the integration of remote sensing-based alteration thematic layers derived from the transformation techniques is a reliable and low-cost approach for mineral prospectivity mapping in metallogenic provinces, at the reconnaissance stage of mineral exploration.

scholarly journals Principal Component Analysis

2021 ◽  
Vol 54 (4) ◽  
pp. 1-34
Author(s):  
Felipe L. Gewers ◽  
Gustavo R. Ferreira ◽  
Henrique F. De Arruda ◽  
Filipi N. Silva ◽  
Cesar H. Comin ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
Real World ◽  
Principal Component ◽  
Component Analysis ◽  
Basic Principles ◽  
Data Standardization ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Real World Datasets

Principal component analysis (PCA) is often applied for analyzing data in the most diverse areas. This work reports, in an accessible and integrated manner, several theoretical and practical aspects of PCA. The basic principles underlying PCA, data standardization, possible visualizations of the PCA results, and outlier detection are subsequently addressed. Next, the potential of using PCA for dimensionality reduction is illustrated on several real-world datasets. Finally, we summarize PCA-related approaches and other dimensionality reduction techniques. All in all, the objective of this work is to assist researchers from the most diverse areas in using and interpreting PCA.

scholarly journals Performance Analysis of Dimensionality Reduction Techniques in the Context of Clustering

2019 ◽  
Vol 8 (S3) ◽  
pp. 66-71
Author(s):  
T. Sudha ◽  
P. Nagendra Kumar
Keyword(s):  
Principal Component Analysis ◽  
Dimensionality Reduction ◽  
High Dimensional Data ◽  
Principal Component ◽  
Component Analysis ◽  
High Dimensional ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Low Dimensional ◽  
Probabilistic Principal Component Analysis

Data mining is one of the major areas of research. Clustering is one of the main functionalities of datamining. High dimensionality is one of the main issues of clustering and Dimensionality reduction can be used as a solution to this problem. The present work makes a comparative study of dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis in the context of clustering. High dimensional data have been reduced to low dimensional data using dimensionality reduction techniques such as t-distributed stochastic neighbour embedding and probabilistic principal component analysis. Cluster analysis has been performed on the high dimensional data as well as the low dimensional data sets obtained through t-distributed stochastic neighbour embedding and Probabilistic principal component analysis with varying number of clusters. Mean squared error; time and space have been considered as parameters for comparison. The results obtained show that time taken to convert the high dimensional data into low dimensional data using probabilistic principal component analysis is higher than the time taken to convert the high dimensional data into low dimensional data using t-distributed stochastic neighbour embedding.The space required by the data set reduced through Probabilistic principal component analysis is less than the storage space required by the data set reduced through t-distributed stochastic neighbour embedding.

scholarly journals Maternal Plasma Metabolic Profile Demarcates a Role for Neuroinflammation in Non-Typical Development of Children

Metabolites ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 545
Author(s):  
Rebecca J. Schmidt ◽  
Donghai Liang ◽  
Stefanie A. Busgang ◽  
Paul Curtin ◽  
Cecilia Giulivi
Keyword(s):  
Dimensionality Reduction ◽  
Principal Component ◽  
Maternal Plasma ◽  
Autism Spectrum ◽  
Plasma Metabolites ◽  
Typical Development ◽  
Reduction Techniques ◽  
Cord Plasma ◽  
Reduction Strategies ◽  
Dimensionality Reduction Techniques

Maternal and cord plasma metabolomics were used to elucidate biological pathways associated with increased diagnosis risk for autism spectrum disorders (ASD). Metabolome-wide associations were assessed in both maternal and umbilical cord plasma in relation to diagnoses of ASD and other non-typical development (Non-TD) compared to typical development (TD) in the Markers of Autism risk in Babies: Learning Early Signs (MARBLES) cohort study of children born to mothers who already have at least one child with ASD. Analyses were stratified by sample matrix type, machine mode, and annotation confidence level. Dimensionality reduction techniques were used [i.e, principal component analysis (PCA) and random subset weighted quantile sum regression (WQSRS)] to minimize the high multiple comparison burden. With WQSRS, a metabolite mixture obtained from the negative mode of maternal plasma decreased the odds of Non-TD compared to TD. These metabolites, all related to the prostaglandin pathway, underscored the relevance of neuroinflammation status. No other significant findings were observed. Dimensionality reduction strategies provided confirming evidence that a set of maternal plasma metabolites are important in distinguishing Non-TD compared to TD diagnosis. A lower risk for Non-TD was linked to anti-inflammatory elements, thereby linking neuroinflammation to detrimental brain function consistent with studies ranging from neurodevelopment to neurodegeneration.

Dimensionality reduction for EEG-based sleep stage detection: comparison of autoencoders, principal component analysis and factor analysis

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Alexandra-Maria Tăuţan ◽  
Alessandro C. Rossi ◽  
Ruben de Francisco ◽  
Bogdan Ionescu
Keyword(s):  
Principal Component Analysis ◽  
Factor Analysis ◽  
Dimensionality Reduction ◽  
Promising Result ◽  
Experimental Testing ◽  
Sleep Stage ◽  
Principal Component ◽  
Component Analysis ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques

AbstractMethods developed for automatic sleep stage detection make use of large amounts of data in the form of polysomnographic (PSG) recordings to build predictive models. In this study, we investigate the effect of several dimensionality reduction techniques, i.e., principal component analysis (PCA), factor analysis (FA), and autoencoders (AE) on common classifiers, e.g., random forests (RF), multilayer perceptron (MLP), long-short term memory (LSTM) networks, for automated sleep stage detection. Experimental testing is carried out on the MGH Dataset provided in the “You Snooze, You Win: The PhysioNet/Computing in Cardiology Challenge 2018”. The signals used as input are the six available (EEG) electoencephalographic channels and combinations with the other PSG signals provided: ECG – electrocardiogram, EMG – electromyogram, respiration based signals – respiratory efforts and airflow. We observe that a similar or improved accuracy is obtained in most cases when using all dimensionality reduction techniques, which is a promising result as it allows to reduce the computational load while maintaining performance and in some cases also improves the accuracy of automated sleep stage detection. In our study, using autoencoders for dimensionality reduction maintains the performance of the model, while using PCA and FA the accuracy of the models is in most cases improved.

COMPARISON OF DIMENSIONALITY REDUCTION TECHNIQUES USING A BACKPROPAGATION NEURAL NETWORK BASED CLASSIFIER

2011 ◽  
Vol 08 (02) ◽  
pp. 161-169
Author(s):  
E. SIVASANKAR ◽  
H. SRIDHAR ◽  
V. BALAKRISHNAN ◽  
K. ASHWIN ◽  
R. S. RAJESH
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Information Gain ◽  
Computation Time ◽  
Principal Component ◽  
Reduction Techniques ◽  
Dimensionality Reduction Techniques ◽  
Credit Risk Analysis

Data mining methods are used to mine voluminous data to find useful information from data. The data that is to be mined may have a large number of dimensions, so the mining process will take a lot of time. In general, the computation time is an exponential function of the number of dimensions. It is in this context that we use dimensionality reduction techniques to speed up the decision-making process. Dimensionality reduction techniques can be categorized as Feature Selection and Feature Extraction Techniques. In this paper we compare the two categories of dimensionality reduction techniques. Feature selection has been implemented using the Information Gain and Goodman–Kruskal measure. Principal Component Analysis has been used for Feature Extraction. In order to compare the accuracy of the methods, we have also implemented a classifier using back-propagation neural network. In general, it is found that feature extraction methods are more accurate than feature selection methods in the framework of credit risk analysis.

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