{"id":1548,"date":"2025-10-03T19:40:24","date_gmt":"2025-10-03T12:40:24","guid":{"rendered":"https:\/\/people.usd.ac.id\/~ydkristanto\/?page_id=1548"},"modified":"2025-10-03T22:57:30","modified_gmt":"2025-10-03T15:57:30","slug":"discover-pca","status":"publish","type":"page","link":"https:\/\/people.usd.ac.id\/~ydkristanto\/index.php\/learning-resources\/discover-pca\/","title":{"rendered":"Discovering PCA: An Interactive Geometric Exploration"},"content":{"rendered":"\n
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Principal Component Analysis (PCA) is often introduced through abstract algebra and matrix computations. We can also approach PCA geometrically: it finds new axes that best capture the variation in data. By experimenting with different directions, we can discover why PCA chooses the particular axes it does.<\/p>\n\n\n\n

This interactive tool lets you explore PCA step by step. First, you can try drawing your own lines and see how well they capture the spread of the data. Then, you can project the data onto the principal components and compare with the original axes. In the end, you\u2019ll see how PCA emerges as a powerful method for reducing data dimensions.<\/p>\n\n\n\n

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PCA Geometry Explorer<\/h3>\n\n\n\n

Let’s uncover the hidden logic of PCA!<\/p>\n\n\n\n

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Window 1: Scatterplot with Adjustable Lines<\/figcaption>\n<\/figure>\n<\/div>\n\n\n\n
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Window 2: Projection on PC Plane<\/figcaption>\n<\/figure>\n<\/div>\n\n\n\n
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Window 3: Variation Explained by Each Axis<\/figcaption>\n<\/figure>\n\n