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All Blog Posts Tagged 'PCA' (2)

Teradata Aster: Principal Component Analysis and Unsupervised Machine Learning

Please watch my video on Aster's principal component analysis or PCA. I not only show how Aster performs this analytic but I attempt to explain how PCA works and explain eigenvectors and eigenvalues. Genre: Statistical Analysis (Unsupervised Learning) Background: A process used to emphasize variability and bring out strong patterns in a dataset. This variability is expressed by principal components; which are directions of highest degree of variance. The first several principal components…

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Added by John Thuma on June 29, 2015 at 2:31am — No Comments

About the Curse of Dimensionality

Introduction



In this article, we will discuss the so called 'Curse of Dimensionality', and explain why it is important when designing a classifier. In the following sections I will provide an intuitive explanation of this concept, illustrated by a clear example of overfitting due to the curse of dimensionality.

Consider an example in which we have a set of images, each of which depicts either a cat or a dog. We would like to create a classifier that is able to…

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Added by Vincent Spruyt on June 6, 2014 at 11:41pm — 3 Comments

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