Hellow guys, Welcome to my website, and you are watching 7. Eckart-Young: The Closest Rank k Matrix to A. and this vIdeo is uploaded by MIT OpenCourseWare at 2019-07-18T12:24:48-07:00. We are pramote this video only for entertainment and educational perpose only. So, I hop you like our website.
| Name | 7. Eckart-Young: The Closest Rank k Matrix to A |
| Video Uploader | Video From MIT OpenCourseWare |
| Upload Date | This Video Uploaded At 18-07-2019 19:24:48 |
| Video Discription | 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: https://www.youtube.com/playlist?list=PLUl4u3cNGP63oMNUHXqIUcrkS2PivhN3k 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. License: Creative Commons BY-NC-SA More information at https://ocw.mit.edu/terms More courses at https://ocw.mit.edu |
| Category | Education |
| Tags | Principle Component Analysis | PCA | Eckart-Young Theorem | low rank approximation | Frobenius Norm |
