Dimensionality Reduction: Why we take Eigenvectors of the Similarity Matrix?
A video breaking down the intuition behind a large family of "spectral" dimensionality reduction algorithms e.g. KPCA, LLE, Laplacian eigenmaps and many others
By Michael Lin
Music: "F*ck That''
-Death Grips
Видео Dimensionality Reduction: Why we take Eigenvectors of the Similarity Matrix? канала Complex Objects
By Michael Lin
Music: "F*ck That''
-Death Grips
Видео Dimensionality Reduction: Why we take Eigenvectors of the Similarity Matrix? канала Complex Objects
Показать
Комментарии отсутствуют
Информация о видео
Другие видео канала
Eigenvectors and eigenvalues | Essence of linear algebra, chapter 14
Laplacian intuition
Dimensionality Reduction - The Math of Intelligence #5
What is an Eigenvector?
The Laplacian Matrices of Graphs: Algorithms and Applications
Dimensionality Reduction: Introduction and Basic Concepts
Lecture 46 — Dimensionality Reduction - Introduction | Stanford University
A Short Introduction to Entropy, Cross-Entropy and KL-Divergence
On Laplacian Eigenmaps for Dimensionality Reduction - Juan Orduz
Machine Learning - Dimensionality Reduction - Feature Extraction & Selection
Dimensionality Reduction
Principal Component Analysis (PCA) clearly explained (2015)
A.I. Experiments: Visualizing High-Dimensional Space
PCA For Dimensionality Reduction in Pattern Recognition, a slecture by Khalid Tahboub
Ali Ghodsi, Lec 6: Spectral Clustering, Laplacian Eigenmap, MVU
Spectral Partitioning, Part 1 The Graph Laplacian
StatQuest: t-SNE, Clearly Explained
Graph Theory: 07 Adjacency Matrix and Incidence Matrix
The things you'll find in higher dimensions
Simply Defined Mathematical Wormhole