Project information
- Category: Machine Learning
- Type: Dimensionality Reduction
- Client/Purpose: This experiment aimed to reproduce the ISOMAP algorithm results from the original paper by Tanenbaum et al.
- Project date: June, 2020
- Project URL: github
Dimensionality reduction in high-dimensional visual space
Reproducting the ISOMAP algorithm on a canonical problem in dimensionality reduction for visual perception. The input consists of many images of a person’s face observed under different pose and lighting conditions, in no particular order. These images can be thought of as points in a high-dimensional vector space, with each input dimension corresponding to the brightness of one pixel in the image.
With the implementation of the ISOMAP the resulting 2-dimensional embedding is more interpretable, at least in one dimension. The experiments used both Euclidian and Manhattan distances as similarity measures while choosing a threshold so that each node has at least 100 neighbors.
Images of faces with similar values in the embeddings were chosen to illustrate the clustering.