This repository contains the implementation of the Kernel ℓ1-norm Principal Component Analysis (KPCA) algorithm for denoising, as described in the paper:
Kernel ℓ1-norm Principal Component Analysis for Denoising
The proposed method focuses on denoising data using KPCA, which combines a projection-free preimage estimation algorithm with an ℓ1-norm KPCA. This approach is insensitive to outliers and computationally efficient, providing better performance in terms of mean squared error compared to the ℓ2-norm KPCA. The algorithm can be applied to a range of unsupervised learning tasks, such as denoising and clustering.
Getting Started Prerequisites
- gcc
- Intel-MKL
Prerequeist: Need install Intel OneAPI. After installation set enviroment variable . /opt/intel/oneapi/setvars.sh
- make clean && make
- go to exec folder
- ./krpca filename #ofcomponents rbf l1 variance