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

In: Advances in Principal Component Analysis

Author

Listed:
  • Padmavathi Ganapathi
  • Roshni Arumugam
  • Shanmugapriya Dhathathri

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

Suggested Citation

  • Padmavathi Ganapathi & Roshni Arumugam & Shanmugapriya Dhathathri, 2022. "Evaluation of Principal Component Analysis Variants to Assess Their Suitability for Mobile Malware Detection," Chapters, in: Fausto Pedro Garcia Marquez (ed.), Advances in Principal Component Analysis, IntechOpen.
    • Handle: RePEc:ito:pchaps:258203
    DOI: 10.5772/intechopen.105418
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    More about this item

    Keywords

    cyber security; dimensionality reduction; machine learning; mobile malware; principal component analysis; variants of PCA;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General

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