Machine learning algorithms | Dimension reduction | Signal processing
In the field of multivariate statistics, kernel principal component analysis (kernel PCA)is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are performed in a reproducing kernel Hilbert space. (Wikipedia).
http://AllSignalProcessing.com for more great signal processing content, including concept/screenshot files, quizzes, MATLAB and data files. Representing multivariate random signals using principal components. Principal component analysis identifies the basis vectors that describe the la
From playlist Random Signal Characterization
Determine the Kernel of a Linear Transformation Given a Matrix (R3, x to 0)
This video explains how to determine the kernel of a linear transformation.
From playlist Kernel and Image of Linear Transformation
Principal Component Analysis (The Math) : Data Science Concepts
Let's explore the math behind principal component analysis! --- Like, Subscribe, and Hit that Bell to get all the latest videos from ritvikmath ~ --- Check out my Medium: https://medium.com/@ritvikmathematics
From playlist Data Science Concepts
19 Data Analytics: Principal Component Analysis
Lecture on unsupervised machine learning with principal component analysis for dimensional reduction, inference and prediction.
From playlist Data Analytics and Geostatistics
Functional Analysis Lecture 19 2014 04 03 : Fundamental Solution of the Heat Operator
Heat kernel; heat operator. Facts about the heat kernel. Fundamental solution of the heat operator. Fundamental solution of a general linear PDE with constant coefficients.
From playlist Course 9: Basic Functional and Harmonic Analysis
08 Machine Learning: Dimensionality Reduction
A lecture on dimensionality reduction through feature selection and feature projection. Includes curse of dimensionality and feature selection review from lecture 5 and summary of methods for feature projection.
From playlist Machine Learning
Data Science for Uncertainty Quantification
Chapter 3 of the book, covers mostly dimension reduction
From playlist Uncertainty Quantification
Computational Model for Sperm Cells Identification and Morphological
For the latest information, please visit: http://www.wolfram.com Speaker: Heidy Hernandez Wolfram developers and colleagues discussed the latest in innovative technologies for cloud computing, interactive deployment, mobile devices, and more.
From playlist Wolfram Technology Conference 2015
2020.05.28 Andrew Stuart - Supervised Learning between Function Spaces
Consider separable Banach spaces X and Y, and equip X with a probability measure m. Let F: X \to Y be an unknown operator. Given data pairs {x_j,F(x_j)} with {x_j} drawn i.i.d. from m, the goal of supervised learning is to approximate F. The proposed approach is motivated by the recent su
From playlist One World Probability Seminar
Kernel Recipes 2022 - Checking your work: validating the kernel by building and testing in CI
The Linux kernel is one of the most complex pieces of software ever written. Being in ring 0, bugs in the kernel are a big problem, so having confidence in the correctness and robustness of the kernel is incredibly important. This is difficult enough for a single version and configuration
From playlist Kernel Recipes 2022
Applied Machine Learning 2019 - Lecture 14 - Dimensionality Reduction
Principal Component Analysis, Linear Discriminant Analysis, Manifold Learning, T-SNE Slides and more materials are on the class website: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
From playlist Applied Machine Learning - Spring 2019
Principal Component Analysis (PCA) - THE MATH YOU SHOULD KNOW!
In this video, we are going to see exactly how we can perform dimensionality reduction with a famous Feature Extraction technique - Principal Component Analysis PCA. We’ll get into the math that powers it REFERENCES [1] Computing Eigen vectors and Eigen values: https://www.scss.tcd.ie/~d
From playlist The Math You Should Know
Similarity of neural network representations revisited
Speaker/author: Simon Kornblith For details including papers and slides, please visit https://aisc.ai.science/events/2019-09-22-similarity-nn-representation
From playlist Natural Language Processing
Applied Machine Learning 2019 - Lecture 16 - NMF; Outlier detection
Non-negative Matrix factorization for feature extraction Outlier detection with probabilistic models Isolation forests One-class SVMs Materials and slides on the class website: https://www.cs.columbia.edu/~amueller/comsw4995s19/schedule/
From playlist Applied Machine Learning - Spring 2019
Determine a Basis for the Kernel of a Matrix Transformation (3 by 4)
This video explains how to determine a basis for the kernel of a matrix transformation.
From playlist Kernel and Image of Linear Transformation
From playlist Plenary talks One World Symposium 2020
