Sign up to save your podcasts
Or
Share Data Science #10 - The original principal component analysis (PCA) paper by Harold Hotelling (1935)
Share to email
Share to Facebook
Share to X
Share to email
Share to Facebook
Share to X
Or
Data Science #10 - The original principal component analysis (PCA) paper by Harold Hotelling (1935)
Hotelling, Harold. "Analysis of a complex of statistical variables into principal components." Journal of educational psychology 24.6 (1933): 417.
This seminal work by Harold Hotelling on PCA remains highly relevant to modern data science because PCA is still widely used for dimensionality reduction, feature extraction, and data visualization.
The foundational concepts of eigenvalue decomposition and maximizing variance in orthogonal directions form the backbone of PCA, which is now automated through numerical methods such as Singular Value Decomposition (SVD).
Modern PCA handles much larger datasets with advanced variants (e.g., Kernel PCA, Sparse PCA), but the core ideas from the paper—identifying and interpreting key components to reduce dimensionality while preserving the most important information—are still crucial in handling high-dimensional data efficiently today.
View all episodes
By Mike E
3
33 ratings
Hotelling, Harold. "Analysis of a complex of statistical variables into principal components." Journal of educational psychology 24.6 (1933): 417.
This seminal work by Harold Hotelling on PCA remains highly relevant to modern data science because PCA is still widely used for dimensionality reduction, feature extraction, and data visualization.
The foundational concepts of eigenvalue decomposition and maximizing variance in orthogonal directions form the backbone of PCA, which is now automated through numerical methods such as Singular Value Decomposition (SVD).
Modern PCA handles much larger datasets with advanced variants (e.g., Kernel PCA, Sparse PCA), but the core ideas from the paper—identifying and interpreting key components to reduce dimensionality while preserving the most important information—are still crucial in handling high-dimensional data efficiently today.
More shows like Data Science Decoded
View all
Science Friday
6,083 Listeners
More or Less: Behind the Stats
891 Listeners
The Quanta Podcast
484 Listeners
Hidden Brain
43,496 Listeners
Space Nuts: Astronomy Insights & Cosmic Discoveries
223 Listeners
Something You Should Know
4,182 Listeners
Super Data Science: ML & AI Podcast with Jon Krohn
296 Listeners
The Daily
110,901 Listeners
Practical AI
190 Listeners
The Origins Podcast with Lawrence Krauss
488 Listeners
The Supermassive Podcast
282 Listeners
Machine Learning Street Talk (MLST)
89 Listeners
The Ancients
2,959 Listeners
The Rest Is Politics
3,124 Listeners
The Bull - Il tuo podcast di finanza personale
22 Listeners