Dimensionality, Linearity and Kernel Functions

Overview

You will learn about dimensionality, linearity and kernel Functions

Goals

• Measure and reduce dimensionality

• Adapt linear models to nonlinear problems

• Learn about Kernel functions

Lecture Materials

• Slides

• Measuring and Reducing Dimensionality

• Adapting Linear Methods to Non-Linear Data and Kernel Functions

Homework Assignment

• Homework 03: K-means and Principle Component Analysis

Supplemental Readings

• Eigenvalue/Eignvector refresher

• Principle Component Analysis

• PCA Step-by-Step

• Blind Signal Separation

• Kernel Method

• Mercer’s Theorem

• Similarity Measure

• Nonlinear Dimensionality Reduction by Locally Linear Embedding