What is PCA in Machine Learning?

4 years ago
Machine Learning

Firstly, this is one of the most important Machine Learning Interview Questions.
In the real world, we deal with multi-dimensional data. Thus, data visualization and computation become more challenging with the increase in dimensions. In such a scenario, we might have to reduce the dimensions to analyze and visualize the data easily. We do this by:

  •  Removing irrelevant dimensions
  •  Keeping only the most relevant dimensions
    This is where we use Principal Component Analysis (PCA).
    Finding a fresh collection of uncorrelated dimensions (orthogonal) and ranking them on the basis of variance are the goals of Principal Component Analysis.
    The Mechanism of PCA:
  • Compute the covariance matrix for data objects
  • Compute the Eigen vectors and the Eigen values in a descending order
  • To get the new dimensions, select the initial N Eigen vectors
  • Finally, change the initial n-dimensional data objects into N-dimensions
    Example: Below are the two graphs showing data points (objects) and two directions: one is ‘green’ and the other is ‘yellow.’ We got the Graph 2 by rotating the Graph 1 so that the x-axis and y-axis represent the ‘green’ and ‘yellow’ directions, respectively.
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Sanisha Maharjan
Sanisha Maharjan
Jan 11, 2022
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