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Understanding Principal Component Analysis: Insights and Applications in Academic Research

Explore the significance of Principal Component Analysis (PCA) in academic research through non-zero mean, entropy intuition, geometric insights, and more. This article by Colin Fyfe delves into the practicality of applying PCA to Iris data and addresses key questions on its relevance and benefits.

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Understanding Principal Component Analysis: Insights and Applications in Academic Research

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  1. GP Principal Component Analysis Colin Fyfe University of Paisley, Scotland

  2. Method • Non-zero mean, bTx+c. • Intuitions about principal components gives target values

  3. Entropy Intuition

  4. Entropy Criterion on Iris Data

  5. Geometric Intuition

  6. Geometric Intuition on Iris Data

  7. Response to Reviewers • Relation to Kernel PCA? • None • Why bother doing PCA with GP ? • I am an academic

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