Principal Component Analysis - PCA

Principal Component Analysis (PCA) is the dimensionality reduction technique which has so many utilities. PCA reduces the dimensions of a dataset by projecting the data onto a lower-dimensional subspace. For example, a 2D dataset could be reduced by projecting the points onto a line. Each instance in the dataset would then be represented by a single value, rather than a pair of values. In a similar way, a 3D dataset could be reduced to two dimensions by projecting variables onto a plane. PCA has the following utilities:

  • Mitigate the course of dimensionality
  • Compress the data while minimizing the information lost at the same time
  • Principal components will be further utilized in the next stage of supervised ...

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