Uploaded July 2023 | Updated September 2026, 1 day ago

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In this video, we dive into unsupervised feature selection techniques, which are essential for reducing the number of features used in machine learning models. Learn about powerful algorithms like PCA, ICA, NMF, TSNE, and autoencoders that help discover important patterns and similarities in data without explicit instructions. Stay tuned for a concise overview of these techniques in under five minutes!

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Here's the full article for both supervised and unsupervised feature selection techniques, including filter-based, wrapper-based, and embedded approaches: stratascratch.com/blog/feature-selection-techniques-in-machine-learning/?utm_source=youtube&utm_medium=click&utm_campaign=YT+unsupervised+feature+selection

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Timeline:

Intro: (0:00​​​)
What is unsupervised Feature selection: (0:42)
Principal Component Analysis: (1:27)
Independent Component Analysis: (2:22)
Non-Negative Matrix Factorization: (2:58)
t-distributed Stochastic Neighbor Embedding: (3:49)
Autoencoder: (4:21)
Conclusion: (4:59)

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