Robust Principal Component Analysis for Feature Extraction of Fire Detection System

Herminarto Nugroho, Muhamad Koyimatu, Ade Irawan, Ariana Yunita


Fire detection system with deep learning-based computer vision (DLCV *) algorithm is proposed in this paper. It uses visible light sensor charged-coupled device (CCD) which can be usually found in closed circuit television camera (CCTV). The performance of this DLCV fire detection depends on how many fire image datasets are trained that might lead to the curse of dimensionality. To tackle the curse of dimensionality, Principal Component Analysis (PCA) will be used. PCA is a technique for feature extraction in which the dimensionality of such datasets is reduced significantly. This will results in increasing interpretability but at the same time minimizing information loss.


principal component analysis; the curse of dimensionality; feature extraction; fire detection system; deep learning


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