Kernel PCA as a Visualization Tools for Clusters Identifications

Nasser, Alissar; Hamad, Denis; Nasr, Chaiban

doi:10.1007/11840930_33

Cited by 8 publications

(7 citation statements)

References 5 publications

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“…Kernel PCA can improve the features of the input data which make easier the separation of clusters. In [17] authors used the scatter plot of a few components of KPCA to determine the number of clusters in the data. By using a nonlinear kernel function i.e.…”

Section: Kernel Principal Components Analysismentioning

confidence: 99%

Security Detection in Audio Events: A Comparison of Classification Methods

Nasser

2020

JAMCS

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The security of public places is becoming important with the increased rate of violence and subversion. Recently, several types of research have been proposed to automatically detect abnormal behavior in public places like a car crash, violence or other hazardous events in an attempt to improve security and save lives. Furthermore, most of the researches are using supervised classifications techniques to classify the audio signals. This paper proposes the use of the kernel principal component analysis (KPCA) to reduce the number of MFCC features extracted from the audio signal and then apply an unsupervised classification algorithm. Moreover, this paper presents the results of several supervised and unsupervised classification methods for audio events detection and compares these results with the result of the proposed approach. Experiments are done using a real data set recorded at the mean of public transportation. The obtained results reveal that K-means on 2 KPCA components gave good results for triggering a true alarm as well as detecting a false alarm; where the percentages of false and missed alarms were 4.5% and 7.8% respectively; whereas these values were 0.8% and 9.3% respectively for kernel k-means. Notwithstanding the DNN network gave the best results with a false alarm rate of 0% and 1.4% missed alarm.

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Section: Kernel Principal Components Analysismentioning

confidence: 99%

Security Detection in Audio Events: A Comparison of Classification Methods

Nasser

2020

JAMCS

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“…We propose to apply kernel PCA projection and kernel Kmeans clustering in the context of audio event detection and identification [8]. We show that kernel functions may extract non linear features suitable for both dimensionality reduction and clustering processes.…”

Section: Introductionmentioning

confidence: 99%

The use of kernel methods for audio events detection

Nasser¹,

Hamad²,

Rouas³

et al. 2008

2008 3rd International Conference on Information and Communication Technologies: From Theory to Applications

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This paper presents an approach for an automatic surveillance system in public transport by analyzing audio signals recorded in the vehicle in order to detect several abnormal behaviors. We try to visualize audio signals by projection methods like PCA and kernel PCA. We use also unsupervised classification (clustering) methods to separate the audio signals into their components precisely we are using Kmeans and kernel K-means.

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“…Lastly, before deciding on the classifier that would achieve the best classification accuracy, visualizing the data can be useful. In [18], Nasser et al demonstrated Kernel PCA as a visualization tool by looking at the scatter plot of the projected data and distinguishing different clusters within the original data. Similarly, in [19], Abid et al proposed contrastive PCA as a tool to identify low-dimensional structures in datasets where data has been collected under different conditions.…”

Section: Introductionmentioning

confidence: 99%

Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis

et al. 2022

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With the recent advances in the field of alternate agriculture, there has been an ever-growing demand for aquaponics as a potential substitute for traditional agricultural techniques for improving sustainable food production. However, the lack of data-driven methods and approaches for aquaponic cultivation remains a challenge. The objective of this research is to investigate statistical methods to make inferences using small datasets for nutrient control in aquaponics to optimize yield. In this work, we employed the Density-Based Synthetic Minority Over-sampling TEchnique (DB-SMOTE) to address dataset imbalance, and ExtraTreesClassifer and Recursive Feature Elimination (RFE) to choose the relevant features. Synthetic data generation techniques such as the Monte-Carlo (MC) sampling techniques were used to generate enough data points and different feature engineering techniques were used on the predictors before evaluating the performance of kernel-based classifiers with the goal of controlling nutrients in the aquaponic solution for optimal growth.[27–35]

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Kernel PCA as a Visualization Tools for Clusters Identifications

Cited by 8 publications

References 5 publications

Security Detection in Audio Events: A Comparison of Classification Methods

Security Detection in Audio Events: A Comparison of Classification Methods

The use of kernel methods for audio events detection

Can Machine Learning classifiers be used to regulate nutrients using small training datasets for aquaponic irrigation?: A comparative analysis

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