Principal Component Analysis (PCA) for Images and Signals

In this video, the theory of Principal Component Analysis (PCA) is explained. The PCA is also known as Hotelling Transform (HT) or Karhunen-Loeve Transform (KLT).
It is widely used in the areas of signals and image processing mainly for size reduction of feature vectors that used for object recognition and classifications. It finds its popularity in Face Recognition.

This video has following contents:
* Introduction to Principal Component Analysis (PCA).
* Dimensionality reduction using PCA and Covariance Matrix.
* Implementation of PCA on Image or signal data set.
* Understanding PCA implementation with example.
* PCA Summary.



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