Variational Autoencoder

A Julia package for manifold learning and nonlinear dimensionality reduction

The key dimensionality reduction techniques: ISOMAP, PCA (Principal Component Analysis), and t-SNE (t-Distributed Stochastic Neighbor Embedding) are presented and compared.

My assignments for homework of Computational Data Mining course at Amirkabir University of Technology

This repository is dedicated to the lab activities of the course of Unsupervised Learning @Units

This project aims to compare the performance obtained using a linear Support Vector Machine model whose data was first processed through a Shortest Path kernel with the same SVM, this time with data also processed by two alternative Manifold Learning techniques: Isomap and Spectral Embedding.

A JavaScript Library for Dimensionality Reduction

Applied Machine Learning (COMP 551) Course Project

This project includes implementations of the MDS and ISOMAP algorithms using Python and various libraries such as NumPy, Matplotlib, Scikit-learn, and NetworkX.

Sklearn, PCA, t-SNE, Isomap, NMF, Random Projection, Spectral Embedding

5th semester project concerning feature engineering and nonlinear dimensionality reduction in particular.

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

Non-linear dimensionality reduction through Isometric Mapping

Python package for plug and play dimensionality reduction techniques and data visualization in 2D or 3D.

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…

a repository for my curriculum project

Visualization and embedding of large datasets using various Dimensionality Reduction (DR) techniques such as t-SNE, UMAP, PaCMAP & IVHD. Implementation of custom metrics to assess DR quality with complete explaination and workflow.

The goal of this project is to understand and build various dimensionality reduction techniques.

The generation of a kmers dataset that is associated with multiple gene sequences and the further manipulation of this generated dataset are the main contents of the current project.

Performing dimensionality reduction with various ML algorithms