Feature reduction of hyperspectral imagery using hybrid wavelet-principal component analysis

2004 ◽  
Vol 43 (2) ◽  
pp. 350 ◽  
Author(s):  
Jacqueline Le Moigne
Keyword(s):  
Principal Component Analysis ◽  
Hyperspectral Imagery ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction

scholarly journals SPCA Assisted Correlation Clustering of Hyperspectral Imagery

2014 ◽  
Vol II-8 ◽  
pp. 111-116 ◽  
Author(s):  
A. Mehta ◽  
O. Dikshit
Keyword(s):  
Principal Component Analysis ◽  
Clustering Algorithm ◽  
Analysis Of Algorithms ◽  
Hyperspectral Imagery ◽  
Principal Component ◽  
Unsupervised Classification ◽  
Component Analysis ◽  
Feature Reduction ◽  
Correlation Clustering ◽  
Selection Of

In this study, correlation clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of correlation clustering lies in its ability to simultaneously perform feature reduction and clustering. This algorithm also allows selection of different sets of features for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. ORCLUS, a correlation clustering algorithm, is implemented and enhanced by making use of segmented principal component analysis (SPCA) instead of principal component analysis (PCA). Further, original implementation of ORCLUS makes use of eigenvectors corresponding to smallest eigenvalues whereas in this study eigenvectors corresponding to maximum eigenvalues are used, as traditionally done when PCA is used as feature reduction tool. Experiments are conducted on three real hyperspectral images. Preliminary analysis of algorithms on real hyperspectral imagery shows ORCLUS is able to produce acceptable results.

Design and Development of EMG Conditioning System and Hand Gesture Recognition Based on Principal Component Analysis Feature Reduction Technique

2014 ◽  
pp. 304-320
Author(s):  
P. Geethanjali
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Digital Signal ◽  
Component Analysis ◽  
Classification Performance ◽  
Feature Reduction ◽  
Design And Development ◽  
Emg Signal ◽  
Knn Classifier ◽  
Power Line Interference

This chapter discusses design and development of a surface Electromyogram (EMG) signal detection and conditioning system along with the issues of gratuitous spurious signals such as power line interference, artifacts, etc., which make signals plausible. In order to construe the recognition of hand gestures from EMG signals, Time Domain (TD) and well as Autoregressive (AR) coefficients features are extracted. The extracted features are diminished using the Principal Component Analysis (PCA) to alleviate the burden of the classifier. A four-channel continuous EMG signal conditioning system is developed and EMG signals are acquired from 10 able-bodied subjects to classify the 6 unique movements of hand and wrist. The reduced statistical TD and AR features are used to classify the signal patterns through k Nearest Neighbour (kNN) as well as Neural Network (NN) classifier. Further, EMG signals acquired from a transradial amputee using 8-channel systems for the 6 amenable motions are also classified. Statistical Analysis of Variance (ANOVA) results on classification performance of able-bodied subject divulge that the performance TD-PCA features are more significant than the AR-PCA features. Further, no significant difference in the performance of NN classifier and kNN classifier is construed with TD reduced features. Since the average classification error of kNN classifier with TD features is found to be less, kNN classifier is implemented in off-line using the TMS2407eZdsp digital signal controller to study the actuation of three low-power DC drives in the identification of intended motion with an able-bodied subject.

Evaluation of Feature Reduction using Principal Component Analysis and Sequential Pattern Matching for Manet

2017 ◽  
Vol 7 (3) ◽  
pp. 1228
Author(s):  
M. Reji ◽  
P.C. Kishore Raja ◽  
Bhagyalakshmi M
Keyword(s):  
Principal Component Analysis ◽  
Ad Hoc ◽  
Pattern Mining ◽  
Principal Component ◽  
Component Analysis ◽  
Feature Reduction ◽  
Sequential Pattern ◽  
Basic Part ◽  
Information Set ◽  
Hoc Networks

In Mobile Ad hoc Networks (MANETs) there are some security problems because of portability, element topology changes, and absence of any framework. In MANETs, it is of extraordinary significance to identify inconsistency and malignant conduct. With a specific end goal to recognize malignant assaults by means of interruption identification frameworks and dissect the information set, we have to choose some components. Thus, highlight determination assumes basic part in recognizing different assaults. In the writing, there are a few recommendations to choose such elements. For the most part, Principal Component Analysis (PCA) breaks down the information set and the chose highlights. In this paper, we have gathered a list of capabilities from some cutting edge works in the writing. Really, our reproduction demonstrates this list of capabilities identify inconsistency conduct more precise. Likewise, interestingly, we utilize PCA for investigating the information set. In contrast to PCA, our results show Sequential pattern mining (SPM) cannot be affected by outlier data within the network. The  normal and attack states are simulated and the results are analyzed using NS2 simulator.

scholarly journals Principal Component Analysis based on data characteristics for dimensionality reduction of ECG recordings in arrhythmia classification

Open Physics ◽  
2019 ◽  
Vol 17 (1) ◽  
pp. 489-496 ◽  
Author(s):  
Agnieszka Wosiak
Keyword(s):  
Principal Component Analysis ◽  
Heart Diseases ◽  
Principal Component ◽  
Feature Space ◽  
Data Interpretation ◽  
Component Analysis ◽  
Feature Reduction ◽  
Analysis Method ◽  
Intrinsic Structure ◽  
Insight Into

Abstract Due to the growing problem of heart diseases, the computer improvement of their diagnostics becomes of great importance. One of the most common heart diseases is cardiac arrhythmia. It is usually diagnosed by measuring the heart activity using electrocardiograph (ECG) and collecting the data as multidimensional medical datasets. However, their storage, analysis and knowledge extraction become highly complex issues. Feature reduction not only enables saving storage and computing resources, but it primarily makes the process of data interpretation more comprehensive. In the paper the new igPCA (in-group Principal Component Analysis) method for feature reduction is proposed. We assume that the set of attributes can be split into subgroups of similar characteristic and then subjected to principal component analysis. The presented method transforms the feature space into a lower dimension and gives the insight into intrinsic structure of data. The method has been verified by experiments done on a dataset of ECG recordings. The obtained effects have been evaluated regarding the number of kept features and classification accuracy of arrhythmia types. Experiment results showed the advantage of the presented method compared to base PCA approach.

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