Vijay Dasari·Sep 25Implementation of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) for…Dimensionality reduction is an important preprocessing technique in machine learning that reduces the number of features while preserving…A response icon1A response icon1
Abhi Sharma·Sep 17Linear Algebra: Basics 3# Principal Component AnalysisIts been sometime now since i am posting about core fundamental topics about linear algebra. Though, I call them Basics but these are…A response icon1A response icon1
InData And BeyondbyDima Iakubovskyi·Sep 10Dimensionality Reduction 101: That UMAP Plot Is Showing You Clusters That Do Not ExistHere, I planted the answer in six datasets, measured six methods against it, and two pieces of the standard warning turned out to be wrongA response icon3A response icon3
Arko Sengupta·Aug 24Singular Value Decomposition: A Comprehensive GuideExplore detailed Singular Value Decomposition
Arko Sengupta·Aug 22t-Distributed Stochastic Neighbor Embedding: A Comprehensive GuideExplore detailed t-Distributed Stochastic Neighbor Embedding
Arko Sengupta·Aug 22Principal Component Analysis: A Comprehensive GuideExplore detailed Principal Component Analysis
Arko Sengupta·Aug 20Dimensionality Reduction: A Comprehensive GuideExplore detailed Dimensionality Reduction
YUNBO GUO·Aug 20Why do PCA and Classical MDS produce the same embedding?Coordinates and pairwise distances look like fundamentally different descriptions of data. The Gram matrix reveals why they lead to the…
Priyanshu·Aug 12Implementing PCA on MNIST: From 784 Pixels to a Meaningful Low-Dimensional RepresentationAfter understanding the intuition, geometry, and mathematics behind PCA, it is time to see what all of that actually looks like when…
