Quantification of Human Intelligence Using Principal Component Analysis

被引：0

作者：

Vignesh, M. Vel ^{[1
]}

Boolog, Vignesh ^{[1
]}

Bagyalakshmi, M. ^{[1
]}

Thilaga, M. ^{[1
]}

机构：

[1] PSG Coll Technol, Coimbatore, Tamil Nadu, India

来源：

COMMUNICATION AND INTELLIGENT SYSTEMS, VOL 1, ICCIS 2023 | 2024年 / 967卷

关键词：

Principal component analysis; Correlation matrix; Dimensionality reduction;

D O I：

10.1007/978-981-97-2053-8_17

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Intelligence Quotient (IQ) classifies individuals into various categories based on their cognitive abilities, and it has been used for a long period of time to quantify a person's intelligence. It has been observed that the majority of earlier studies employ IQ for diagnosis and assessment of intellectual disability but do not disclose how IQ was calculated using raw data. In this paper, we present a novel method that uses Principle Component Analysis (PCA) to quantify IQ of individuals from raw data obtained through IQ tests. The proposed method was used to examine the IQ of a subset of diverse group of individuals, rather than using a homogeneous group with a large sample size, to determine even the smallest variations in their cognitive abilities. The computational method proposed in this paper can be used a statistical tool for IQ measurement.

引用

页码：225 / 237

页数：13

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