Explaining Singular Value Decomposition

Tim Bock explains how Singular Value Decomposition works:

The table above is a matrix of numbers. I am going to call it Z. The singular value decomposition is computed using the svd function. The following code computes the singular value decomposition of the matrix Z, and assigns it to a new object called SVD, which contains one vector, d, and two matrices, u and v. The vector, d, contains the singular values. The first matrix, u, contains the left singular vectors, and vcontains the right singular vectors. The left singular vectors represent the rows of the input table, and the right singular vectors represent their columns.

Tim includes R scripts to follow along, and for this topic I definitely recommend following along.

Related Posts

Interpreting The Area Under The Receiver Operating Characteristic Curve

Roos Colman explains what a Receiver Operating Characteristic (ROC) curve is and how we interpret the Area Under the Curve (AUC): The AUC can be defined as “The probability that a randomly selected case will have a higher test result than a randomly selected control”. Let’s use this definition to calculate and visualize the estimated […]

Read More

Building A Neural Network In R With Keras

Pablo Casas walks us through Keras on R: One of the key points in Deep Learning is to understand the dimensions of the vector, matrices and/or arrays that the model needs. I found that these are the types supported by Keras. In Python’s words, it is the shape of the array. To do a binary […]

Read More

Categories

August 2017
MTWTFSS
« Jul Sep »
 123456
78910111213
14151617181920
21222324252627
28293031