Search for tag: "pca"

ML3: Dimensionality Reduction using Principal Component Analysis

In this video, we will learn that we can use Principal Component Analysis (PCA) to find a transformation matrix that minimises reconstruction error for dimensionality reduction. We also show how this…

From  Elliot Crowley 0 likes 20 plays 0  

MLP Lecture 06 - Clip 03 - Data Normalisation

Machine Learning Practical (MLP) Lecture 06, Clip 03 / 05.

From  Pavlos Andreadis 1 likes 386 plays 0  

Unsupervised Learning

Unsupervised Learning

From  Dimitrios Doudesis 0 likes 290 plays 0  

Properties of eigenfaces

Properties of eigenfaces

From  Nigel Goddard 1 likes 1,831 plays 0  

Pros and cons of dimensionality reduction

Pros and cons of dimensionality reduction

From  Nigel Goddard 1 likes 2,589 plays 0  

Principal component analysis

Principal component analysis

From  Nigel Goddard 1 likes 2,742 plays 0  

Linear discriminant analysis

Linear discriminant analysis

From  Nigel Goddard 1 likes 2,007 plays 0  

Classification with PCA features

Classification with PCA features

From  Nigel Goddard 1 likes 2,118 plays 0  

When principal components fail

When principal components fail

From  Nigel Goddard 1 likes 1,998 plays 0  

Eigenface representation

Eigenface representation

From  Nigel Goddard 1 likes 2,046 plays 0  

Eigen-faces

Eigen-faces

From  Nigel Goddard 1 likes 2,287 plays 0  

Principal component analysis for the impatient

Principal component analysis for the impatient

From  Nigel Goddard 1 likes 2,155 plays 0  

How many principal components to use

How many principal components to use

From  Nigel Goddard 1 likes 2,138 plays 0  

Eigenvalue = variance along eigenvector

Eigenvalue = variance along eigenvector

From  Nigel Goddard 1 likes 2,204 plays 0  

Eigenvector = direction of maximum variance

Eigenvector = direction of maximum variance

From  Nigel Goddard 2 likes 2,731 plays 0  

Low-dimensional projections of data

Low-dimensional projections of data

From  Nigel Goddard 0 likes 2,594 plays 0