Principal component analysis (PCA) is a classical machine learning technique. The goal of PCA is to transform a dataset into one with fewer columns. This is called dimensionality reduction. The ...

Single-cell RNA-seq AI analysis has become the default way to make sense of the millions of expression measurements a single experiment can now generate. Turning raw sequencing counts into ...

PCA is an important tool for dimensionality reduction in data science and to compute grasp poses for robotic manipulation from point cloud data. PCA can also directly used within a larger machine ...