7. Eckart-Young: The Closest Rank k Matrix to A

Описание к видео 7. Eckart-Young: The Closest Rank k Matrix to A

MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning, Spring 2018
Instructor: Gilbert Strang
View the complete course: https://ocw.mit.edu/18-065S18
YouTube Playlist:    • MIT 18.065 Matrix Methods in Data Ana...  

In this lecture, Professor Strang reviews Principal Component Analysis (PCA), which is a major tool in understanding a matrix of data. In particular, he focuses on the Eckart-Young low rank approximation theorem.

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