A Combined Framework for Dimensionality Reduction of Hyperspectral Images using Feature Selection and Feature Extraction

2019 ◽  
Author(s):  
Susmita Ghosh ◽  
Payel Pramanik
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Hyperspectral Images

scholarly journals An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images

2019 ◽  
Vol 11 (2) ◽  
pp. 136 ◽  
Author(s):  
Yuliang Wang ◽  
Huiyi Su ◽  
Mingshi Li
Keyword(s):  
Feature Extraction ◽  
Logistic Regression ◽  
Dimensionality Reduction ◽  
Spectral Feature ◽  
Hyperspectral Images ◽  
Nonlinear Dimensionality Reduction ◽  
Impervious Surfaces ◽  
Urban Impervious Surfaces ◽  
Improved Model ◽  
Logistic Regression Classifier

Hyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding (t-SNE), and the deep learning method convolutional deep belief networks (CDBNs). We improved the two methods to create a novel MFEM consisting of improved t-SNE, deep compression CDBNs (d-CDBNs), and a logistic regression classifier. The improved t-SNE method provides dimensionality reduction and spectral feature extraction from the original HSIs and the d-CDBNs algorithm extracts spatial feature and edges using the reduced dimensional datasets. Finally, the extracted features are combined into multi-feature for the impervious surface detection using the logistic regression classifier. After comparing with the commonly used methods, the current experimental results demonstrate that the proposed MFEM model provides better performance for UIS extraction and detection from HSIs.

scholarly journals Heuristic Search Algorithm for Dimensionality Reduction Optimally Combining Feature Selection and Feature Extraction

2019 ◽  
Vol 33 ◽  
pp. 2280-2287 ◽  
Author(s):  
Baokun He ◽  
Swair Shah ◽  
Crystal Maung ◽  
Gordon Arnold ◽  
Guihong Wan ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Heuristic Search ◽  
Search Algorithm ◽  
A Priori ◽  
Optimal Solution ◽  
Approximate Solutions ◽  
Combinatorial Search ◽  
Heuristic Search Algorithm

The following are two classical approaches to dimensionality reduction: 1. Approximating the data with a small number of features that exist in the data (feature selection). 2. Approximating the data with a small number of arbitrary features (feature extraction). We study a generalization that approximates the data with both selected and extracted features. We show that an optimal solution to this hybrid problem involves a combinatorial search, and cannot be trivially obtained even if one can solve optimally the separate problems of selection and extraction. Our approach that gives optimal and approximate solutions uses a “best first” heuristic search. The algorithm comes with both an a priori and an a posteriori optimality guarantee similar to those that can be obtained for the classical weighted A* algorithm. Experimental results show the effectiveness of the proposed approach.

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.

scholarly journals New Application of Life Rank Algorithm: A Case Study

2021 ◽  
Vol 23 (06) ◽  
pp. 438-447
Author(s):  
Neha Sharma ◽  
Dr. RashiAgarwal ◽  
Dr. NarendraKohli ◽  
Dr. Shubha Jain
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Learning To Rank ◽  
Feature Reduction ◽  
Linear Feature ◽  
Redundant Data ◽  
Reduction Techniques ◽  
Before And After ◽  
Reduction Methods

The past few years have seen the emergence of learning-to-rank (LTR) in the field of machine learning. In information acquiring the size of data is very large and empowering a learning-to-rank model on it will be a costly and time taking process. High dimension data leads to irrelevant and redundant data which results in overfitting. “Dimensionality reduction” methods are used to manage this issue. There are two-dimensionality reduction techniques namely feature selection and feature reduction. There is extensive research available on the algorithm for learning-to-rank but this not the case for dimensionality reduction approaches in LTR, despite its importance. Feature selection techniques for classification are directly used for ranking. To the best of our understanding, feature extraction techniques in the context of ranking problems are not explored much to date. So, we make an effort to fill this void and explore feature extraction in the context of LTR problems. The LifeRank algorithm is a linear feature extraction algorithm for ranking. Its performance is analyzed on RankSVM and Linear regression. It is not applied to other learning-to-rank algorithms. So, in this task, an attempt is made to study the effect of the application of the LifeRank algorithm on other LTR algorithms. LifeRank algorithm is applied on RankNet and RankBoost. Then, the performance of several LTR algorithms on the LETOR dataset is analyzed before and after feature extraction.

scholarly journals FSEFST:Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data

2019 ◽  
Vol 9 (2) ◽  
pp. 814-820
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Efficient Algorithm ◽  
High Dimensional Data ◽  
Suggested Method ◽  
High Dimensional ◽  
Feature Subset

Dimensionality reduction is one of the pre-processing phases required when large amount of data is available. Feature selection and Feature Extraction are one of the methods used to reduce the dimensionality. Till now these methods were using separately so the resultant feature contains original or transformed data. An efficient algorithm for Feature Selection and Extraction using Feature Subset Technique in High Dimensional Data (FSEFST) has been proposed in order to select and extract the efficient features by using feature subset method where it will have both original and transformed data. The results prove that the suggested method is better as compared with the existing algorithm

scholarly journals A Comprehensive Review of Dimensionality Reduction Techniques for Feature Selection and Feature Extraction

2020 ◽  
Vol 1 (2) ◽  
pp. 56-70 ◽  
Author(s):  
Rizgar Zebari ◽  
Adnan Abdulazeez ◽  
Diyar Zeebaree ◽  
Dilovan Zebari ◽  
Jwan Saeed
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Computational Time ◽  
High Dimensions ◽  
Training Time ◽  
Reduction Techniques ◽  
Processing Step ◽  
Dimensionality Reduction Techniques ◽  
Processing And Storage

Due to sharp increases in data dimensions, working on every data mining or machine learning (ML) task requires more efficient techniques to get the desired results. Therefore, in recent years, researchers have proposed and developed many methods and techniques to reduce the high dimensions of data and to attain the required accuracy. To ameliorate the accuracy of learning features as well as to decrease the training time dimensionality reduction is used as a pre-processing step, which can eliminate irrelevant data, noise, and redundant features. Dimensionality reduction (DR) has been performed based on two main methods, which are feature selection (FS) and feature extraction (FE). FS is considered an important method because data is generated continuously at an ever-increasing rate; some serious dimensionality problems can be reduced with this method, such as decreasing redundancy effectively, eliminating irrelevant data, and ameliorating result comprehensibility. Moreover, FE transacts with the problem of finding the most distinctive, informative, and decreased set of features to ameliorate the efficiency of both the processing and storage of data. This paper offers a comprehensive approach to FS and FE in the scope of DR. Moreover, the details of each paper, such as used algorithms/approaches, datasets, classifiers, and achieved results are comprehensively analyzed and summarized. Besides, a systematic discussion of all of the reviewed methods to highlight authors' trends, determining the method(s) has been done, which significantly reduced computational time, and selecting the most accurate classifiers. As a result, the different types of both methods have been discussed and analyzed the findings.  

Feature Selection

2011 ◽  
pp. 632-638
Author(s):  
Noelia Sánchez-Maroño ◽  
Amparo Alonso-Betanzos
Keyword(s):  
Feature Extraction ◽  
Feature Selection ◽  
Dimensionality Reduction ◽  
Data Compression ◽  
Learning Algorithms ◽  
Linear Mapping ◽  
Modelling And Simulation ◽  
Scientific Disciplines ◽  
Processing Step ◽  
Computational Resources

Many scientific disciplines use modelling and simulation processes and techniques in order to implement non-linear mapping between the input and the output variables for a given system under study. Any variable that helps to solve the problem may be considered as input. Ideally, any classifier or regressor should be able to detect important features and discard irrelevant features, and consequently, a pre-processing step to reduce dimensionality should not be necessary. Nonetheless, in many cases, reducing the dimensionality of a problem has certain advantages (Alpaydin, 2004; Guyon & Elisseeff, 2003), as follows: • Performance improvement. The complexity of most learning algorithms depends on the number of samples and features (curse of dimensionality). By reducing the number of features, dimensionality is also decreased, and this may save on computational resources—such as memory and time—and shorten training and testing times. • Data compression. There is no need to retrieve and store a feature that is not required. • Data comprehension. Dimensionality reduction facilitates the comprehension and visualisation of data. • Simplicity. Simpler models tend to be more robust when small datasets are used. There are two main methods for reducing dimensionality: feature extraction and feature selection. In this chapter we propose a review of different feature selection (FS) algorithms, including its main approaches: filter, wrapper and hybrid – a filter/wrapper combination.

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