Evaluation of Principal Component Analysis Variants to Assess Their Suitability for Mobile Malware Detection

Padmavathi Ganapathi, Roshni Arumugam and Shanmugapriya Dhathathri

A chapter in Advances in Principal Component Analysis from IntechOpen

Abstract: Principal component analysis (PCA) is an unsupervised machine learning algorithm that plays a vital role in reducing the dimensions of the data in building an appropriate machine learning model. It is a statistical process that transforms the data containing correlated features into a set of uncorrelated features with the help of orthogonal transformations. Unsupervised machine learning is a concept of self-learning method that involves unlabelled data to identify hidden patterns. PCA converts the data features from a high dimensional space into a low dimensional space. PCA also acts as a feature extraction method since it transforms the 'n' number of features into 'm' number of principal components (PCs; m

Keywords: cyber security; dimensionality reduction; machine learning; mobile malware; principal component analysis; variants of PCA (search for similar items in EconPapers)
JEL-codes: C10 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:ito:pchaps:258203

DOI: 10.5772/intechopen.105418

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