Comparing dimensionality reduction techniques

Nick, William; Shelton, Joseph; Bullock, Gina; Esterline, Albert; Asamene, Kassahun

doi:10.1109/secon.2015.7132997

Cited by 5 publications

(4 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Joshi and Machchhar [18] conduct a comprehensive survey on DR methods and proposed a DR method that depends upon the given set of parameters and varying conditions [18]. The authors investigate that recursive feature elimination, and genetic and evolutionary feature weighting and selection give better classification result than PCA [19].…”

Section: Related Workmentioning

confidence: 99%

PCA-based dimensionality reduction for face recognition

et al. 2021

View full text Add to dashboard Cite

In this paper, we conduct a comprehensive study on dimensionality reduction (DR) techniques and discuss the mostly used statistical DR technique called principal component analysis (PCA) in detail with a view to addressing the classical face recognition problem. Therefore, we, more devotedly, propose a solution to either a typical face or individual face recognition based on the principal components, which are constructed using PCA on the face images. We simulate the proposed solution with several training and test sets of manually captured face images and also with the popular Olivetti Research Laboratory (ORL) and Yale face databases. The performance measure of the proposed face recognizer signifies its superiority.

show abstract

Section: Related Workmentioning

confidence: 99%

PCA-based dimensionality reduction for face recognition

et al. 2021

View full text Add to dashboard Cite

show abstract

“…PCA is one of the feature extraction techniques for extracting important features (called components) from a large set of the dataset [1,18]. With the PCA techniques, a set-off element in the low dimensional dataset is extricated from a high-dimensional dataset in a bid to get the maximum of information.…”

Section: Principle Component Analysis (Pca)mentioning

confidence: 99%

A novel approach for big data classification based on hybrid parallel dimensionality reduction using spark cluster

Ali¹,

Abdullah²

2019

csci

View full text Add to dashboard Cite

The big data concept has elicited studies on how to accurately and efficiently extract valuable information from such huge dataset. The major problem during big data mining is data dimensionality due to a large number of dimensions in such datasets. This major consequence of high data dimensionality is that it affects the accuracy of machine learning (ML) classifiers; it also results in time wastage due to the presence of several redundant features in the dataset. This problem can be possibly solved using a fast feature reduction method. Hence, this study presents a fast HP-PL which is a new hybrid parallel feature reduction framework that utilizes spark to facilitate feature reduction on shared/distributed-memory clusters. The evaluation of the proposed HP-PL on KDD99 dataset showed the algorithm to be significantly faster than the conventional feature reduction techniques. The proposed technique required 1 minute to select 4 dataset features from over 79 features and 3,000,000 samples on a 3-node cluster (total of 21 cores). For the comparative algorithm, more than 2 hours was required to achieve the same feat. In the proposed system, Hadoop’s distributed file system (HDFS) was used to achieve distributed storage while Apache Spark was used as the computing engine. The model development was based on a parallel model with full consideration of the high performance and throughput of distributed computing. Conclusively, the proposed HP-PL method can achieve good accuracy with less memory and time compared to the conventional methods of feature reduction. This tool can be publicly accessed at https://github.com/ahmed/Fast-HP-PL.

show abstract

“…used unsupervised learning for acoustic emission signals to identify the presence and location of damage and then switched to supervised learning to identify the type and severity of faults. [ 11 ] These methods are able to identify the fault types and have a strong resistance to overfitting, but they are unable to achieve the desired detection effect and have poor robustness when facing the complex background environment of wind power airports. Deng et al.…”

Section: Introductionmentioning

confidence: 99%

“…[10] Nick et al used unsupervised learning for acoustic emission signals to identify the presence and location of damage and then switched to supervised learning to identify the type and severity of faults. [11] These methods are able to identify the fault types and have a strong resistance to overfitting, but they are unable to achieve the desired detection effect and have poor robustness when facing the complex background environment of wind power airports. Deng et al proposed an adaptive filter based on improved LPSO algorithm and log-Gabor filter using HOG+SVM classifier to determine the types of defects in wind turbine blade images, and the method can effectively obtain the feature information of defects.…”

Section: Introductionmentioning

confidence: 99%

SOD‐YOLO: A Small Target Defect Detection Algorithm for Wind Turbine Blades Based on Improved YOLOv5

Zhang

Wen

2022

Advcd Theory and Sims

View full text Add to dashboard Cite

Early and effective detection of wind turbine blade (WTB) surface defects can avoid complex and expensive repair problems and serious safety hazards. The traditional target detection methods have the problems of insufficient detection capability, long model inference time and low recognition accuracy for small targets and long strip defects in WTB datasets. This paper proposes a high‐precision model SOD‐YOLO for WTB surface defect detection based on UAVs image analysis of YOLOv5. First, the WTB images are preprocessed by foreground segmentation and Hough transform to build the WTB defect dataset. Then, a micro‐scale detection layer is added to the original YOLOv5, and the K‐means algorithm is used to re‐cluster anchors and add the CBAM attention mechanism to each feature fusion layer to reduce the loss of feature information for small target defects and other defects. In addition, to improve the detection efficiency, the channel pruning algorithm is used to reduce the model size. The experimental results show that the average accuracy (mAP) of the SOD‐YOLO algorithm on the WTB dataset reaches 95.1%, which is 7.82% better than YOLOv5, and the FPS is 28.3% better. Therefore, SOD‐YOLO is able to detect small target defects and other defects quickly and effectively.

show abstract

Comparing dimensionality reduction techniques

Cited by 5 publications

References 3 publications

PCA-based dimensionality reduction for face recognition

PCA-based dimensionality reduction for face recognition

A novel approach for big data classification based on hybrid parallel dimensionality reduction using spark cluster

SOD‐YOLO: A Small Target Defect Detection Algorithm for Wind Turbine Blades Based on Improved YOLOv5

Contact Info

Product

Resources

About