Simplify complex datasets using Principal Component Analysis (PCA) in Python. Great for dimensionality reduction and ...

A growing number of home renovation and interior design platforms are rolling out A.I.-enabled imaging tools capable of redesigning rooms in an instant. By Rachel Wharton When Lee Mayer launched the ...

Hyperspectral imaging (HSI) captures rich spectral data across hundreds of contiguous bands for diverse applications. Dimension reduction (DR) techniques are commonly used to map the first three ...

Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets ...

Abstract: Principal Component Analysis (PCA) is perhaps the most popular linear projection technique for dimensionality reduction. We consider PCA under the assumption that the high-dimensional data ...

Harrison Barnes used a visualization tool to help him improve. Illustration: Dan Goldfarb / The Athletic; Logan Riely / NBAE / Getty Images Editor’s note: This story is part of Peak, The Athletic’s ...

Pull requests help you collaborate on code with other people. As pull requests are created, they’ll appear here in a searchable and filterable list. To get started, you should create a pull request.

To continue reading this content, please enable JavaScript in your browser settings and refresh this page. Preview this article 1 min One of the nation's largest ...

1 University of Dallas, Computer Science Department, Irving, TX, United States 2 University of Dallas, Biology Department, Irving, TX, United States T-cell receptor (TCR) sequencing has emerged as a ...

This is the final installment of a three-part series marking the 10th anniversary of the historic sentencing in the Peanut Corporation of America (PCA) case. To read Part 1, click here. To read Part 2 ...