Dimensionality Reduction: Seeing What 40 Features Look Like

Why the curse of dimensionality hurts algorithms like k-means on wide feature tables, PCA's fast and meaningful projection onto directions of greatest variance, t-SNE's visually cleaner but non-metric alternative, and a real side-by-side comparison on an NSL-KDD-style dataset.

~5 min total·4 quadrants of structured content

By the end of this topic, you will

Explain the curse of dimensionality and why it degrades distance-based algorithms like k-means
Project a high-dimensional feature set down to two dimensions with PCA and read its explained variance ratio
Explain why t-SNE should not be used as the input to a clustering algorithm, only as a visualisation afterward

Quadrant 1 · e-Tutorial

Video lectures and walkthroughs

Video content coming soon.

Quadrant 2 · e-Content

Articles and case studies

Dimensionality Reduction: Seeing What 40 Features Look Like

Humans cannot plot forty dimensions, and many algorithms perform worse as dimensionality grows. PCA and t-SNE project the same data down to two dimensions in very different ways, and this unit runs both, side by side, on the same real dataset.

Quadrant 3 · Web Resources