Feature Dimensions
Overview
Feature dimensions refers to the number of degrees of freedom that a sample datapoint has. In the
standard case, a data point is represented by a vector
{% \vec{v} \in \mathbb{R}^n %}
That is, the vector would look like a
column vector
{%
\begin{bmatrix}
a \\
c \\
e \\
\end{bmatrix}
%}
Dimensions is often synonomous with
features.
Dimension Reduction
Dimension Reduction -
When the number of dimensions of dataset is large compared to the number of datapoints, it is often hard for most
analytical techniques to arrive at a robust answer. The solution is often to try to find a way to
reduce the number of dimensions
This is marked contrast to the
technique of expanding the dimensions in order to create a linearly separable pattern.
(a technique used in machine learning)
(see
linearing patterns with extra dimensions
and
kernel trick)
