Principal component analysis (PCA, 주성분분석) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components. ( https://en.wikipedia.org/wiki/Principal_component_analysis)

대표적인 Dimension reduction 방법. 서로간에 가장 분산을 크게하는 두개의 축을 찾는다.

관련정보

• PCA(주성분분석)의 의미와 한계점 2015-12-01
• Principal Component Analysis in 3 Simple Steps 2015-01-17: 쉬운 설명과 scikit-learn 예제

Incoming Links #

Related Articles (Article 0) #

• Factor analysis
• Feature selection
• Linear discriminant analysis
• Microsatellite

Related Medical Scholarly Articles (MedicalScholarlyArticle 1) #

• An evaluation of copy number variation detection tools from whole-exome sequencing data
• Methods of integrating data to uncover genotype–phenotype interactions
• Principal component analysis reveals the 1000 Genomes Project does not sufficiently cover the human genetic diversity in Asia

Related Codes (Code 2) #

• Hail
• NarrowPeaks
• scikit-learn

Suggested Pages #

• 0.121 Genetic variation
• 0.100 Challenges in analysis and interpretation of microsatellite data for population genetic studies
• 0.078 SNV
• 0.077 Microsatellite instability
• 0.045 Machine learning
• 0.043 OMICStools
• 0.037 Scikit Flow
• 0.033 DNA profiling
• 0.033 Artificial neural network
• 0.027 Hidden Markov model
• More suggestions...