Episode

Principal Components Analysis in TypeScript (Part 4): Turning PCA Into Interpretable Factor Analysis

Podcast
Data Science Tech Brief By HackerNoon
Published
May 30, 2026
Duration seconds
325
Processing state
not_requested
Canonical source
https://share.transistor.fm/s/7b0138d6
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https://media.transistor.fm/7b0138d6/7373041f.mp3
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Summary

This story was originally published on HackerNoon at: https://hackernoon.com/principal-components-analysis-in-typescript-part-4-turning-pca-into-interpretable-factor-analysis . Remember how PCA collapses data with 100 dimensions into a single dimension, wouldn't it be cool if this dimension were interpretable. Factor Analysis does that Check more stories related to data-science at: https://hackernoon.com/c/data-science . You can also check exclusive content about #data-analysis , #typescript , #principal-component-analysis , #factor-analysis , #singular-value-decomposition , #interpretable-ai , #dimensionality-reduction , #exploratory-data-analysis , and more. This story was written by: @bitanath . Learn more about this writer by checking @bitanath's about page, and for more stories, please visit hackernoon.com . Now remember how PCA collapses data with 100 dimensions into a single dimension, wouldn't it be cool if this dimension was interpretable. For example, let's say the 100 columns were like stress, smoking frequency, alcohol ml etc etc.. you see where I am going with this, the final dimension would be something like cardiac arrest or premature demise. On that cheery note, let's figure out how PCA can actually be used to label this reduced dimension.