Principal component analysis (PCA) is one of the most valuable results from applied linear algebra, and probably the most popular method used for compacting higher dimensional data sets into lower dimensional ones for data analysis, visu-alization, feature extraction, or data compression

• The purpose of considering this algorithm here is three-fold:
  • it gives additional insight into what the loadings and scores mean;
  • it shows how each component is independent of (orthogonal to) the other components,
  • it shows how the algorithm can handle missing data.
• The algorithm extracts each component sequentially, starting with the first component, direction of greatest variance, and then the second component, and so on.

Checkout this link and this link for a more detailed view of the algorithm.

The purpose is to implement the paper :link on GPU using pyCuda.

You'll need Numpy, Sklearn for running and testing the CPU verion.

For running the GPU version, you'll need a Linux or Windows 10 PC with a modern NVIDIA GPU (>=2016) is required, with all necessary GPU drivers and the CUDA Toolkit (10.0 onward) installed. A suitable Python (>=3) with the PyCUDA module is also required. 

For testing the kernel functions in tests/, run the following:

pip install -e .
python -m unittest -v tests/test_nipals_gpu.py

The kernel functions in test_kernels/ are an optimized version of the operations in nipals/kernels.py but are not stable yet.