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| From https://www.youtube.com/watch?v=spUNpyF58BY |
29 Feb and 7 Mar, 2024
Mohammad Sayeh
On Fourier and fractional Fourier transforms, and more.
Video of Part 1
Video of Part 2 (no sound...)
Wednesday, February 28, 2024
Spring 2024
29 Feb and 7 Mar, 2024
Tuesday, February 20, 2024
Spring 2024
Mathew Gluck
Introduction to Principal Component Analysis
Abstract: Principal component analysis (PCA) is a data dimensionality reduction technique whose goal is to throw away the least essential components of a data set. It is commonly used by data science practitioners whose data belongs to a vector space of large dimensions. It is well-known that the eigenpairs of a suitable covariance matrix “know” which components of data are most important and which are least important. However, in many expositions on PCA, the reason behind this well-known fact is rarely explained. I will offer an explanation and I will show a concrete application of PCA in image data.
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