Manifold-based Dimensionality Reduction Methods

This repository contains Python implementations of two manifold-based dimensionality reduction methods based on the following research papers:

1. Sparsity Preserving Projections with Applications to Face Recognition
2. Discriminant Sparse Neighborhood Preserving Embedding for Face Recognition

These implementations were reconstructed as part of my thesis work.

Methods Implemented

1. Sparsity Preserving Projections (SPP):

   • Description: SPP is a dimensionality reduction method that preserves the sparsity structure of data, which is particularly useful for face recognition applications.
   • Implementation: SPP_method.py

2. Discriminant Sparse Neighborhood Preserving Embedding (DSNPE):

   • Description: DSNPE is a technique designed for face recognition that combines discriminant analysis with sparsity preserving projections.
   • Implementation: DSNPE_method.py

Usage

To use these dimensionality reduction methods in your Python projects, you can simply import the respective modules:

from SPP_method import SPP
from DSNPE_method import DSNPE

#SPP use
spp = SPP(n_components=50, epsilon=0.05)
spp.fit(X)
X_spp = dsnpe.transform(X)

#DSNPE use
dsnpe = DSNPE(n_components=50, epsilon=0.05, gamma=1.0)
dsnpe.fit(X, y)
X_dsnpe = dsnpe.transform(X)