Research/Blog
Principal Component Analysis (PCA) in Artificial Intelligence
- December 6, 2019
- Posted by: CellStrat Editor
- Category: Artificial Intelligence Deep Learning Machine Learning Technology
Artificial Intelligence applications are taking over the world in almost all the possible areas known and to for artificial intelligence to be developed, machine learning lrequires lots and lots of training data, more the better. So large data sets are though useful but on the flip side, using a large data set has its own pitfalls as many a times it carries lot of redundant values too or unusable data. In other words, it suffers from a curse of dimensionality.
It turns out that in large dimensional datasets, there might be lots of inconsistencies in the features or lots of redundant features in the dataset, which will only increase the computation time and make data processing more complicated.
A process called ‘dimensionality reduction’ was introduced to get rid of the curse of dimensionality. Dimensionality reduction techniques can be used to filter only a limited number of significant features needed for training and this is where Principal Component Analysis (PCA) comes in.
PCA figures out patterns and correlations among various features in the dataset. On finding a strong correlation between different variables, a final decision is made about reducing the dimensions of the data in such a way that the required data is still retained.
Such a process is very essential in solving complex data-driven problems that involve the use of high-dimensional data sets.