PCA: Principal Component Analysis - Dimensionality Reduction

Chapter I: Descriptive Analysis
(Unsupervised Learning:
Dimensionality Reduction)
I
-
1
: Principal Component Analysis
PLAQUETTE COMMERCIALE
: Principal Component Analysis
(PCA)

IN
Dimension Reduction :2D ---1D
Introduction:

Dimension Reduction : 3D --2D
Introduction:
In the same Way, we can reduce dimension
1000
D
to
100
D,

Dimensionality Reduction:
Dimensionality reduction aims to summarize
data by assigning it a new representation that
highlights what is hidden by the volume of data.
This
involves
transforming
of
initial
This
involves
transforming
a
set
of
initial
variables, some of which are correlated, into a
set of uncorrelated variables called principal
components, while preserving as much data
variance as possible, i.e., the essential
information.

Why Reduce Data Dimensionality?
Data compression:
Saves memory space.
Makes the learning algorithm faster.
Improves algorithm performance.
Improves algorithm performance.
Data visualization.