Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can…

The code for Generative Locally Linear Embedding (GLLE).

Official code of Image Fusion Through Linear Embeddings (IEEE ICIP 21)

The code for Locally Linear Image Structural Embedding (LLISE) and Kernel LLISE

Use Manifold Learning, Mapping and Discriminant Analysis to Visualize Image Datasets

This repository explores the interplay between dimensionality reduction techniques and classification algorithms in the realm of breast cancer diagnosis. Leveraging the Breast Cancer Wisconsin dataset, it assesses the impact of various methods, including PCA, Kernel PCA, LLE, UMAP, and Supervised UMAP, on the performance of a Decision Tree.

Official Implementation of Multi-Exposure Image Fusion based on Linear Embeddings and Watershed Masking

Improve Data Quality by discarding non-correlating, noisy Dimensions

Matrix, Tensor, Linear and Nonlinear Numerical Methods in Data Analysis course.

Concepts in Manifold Learning and Spectral Clustering Techniques