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 discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract) contribution and that of the excitation. Taking into account the characteristics of the human ear, the mel-frequency cepstral coefficients (MFCCs) emerged as descriptive features of the speech spectral envelope. Similarly to MFCCs, the perceptual linear prediction coefficients (PLPs) could also be derived. The aforementioned sort of speaking tradi- tional features will be tested against agnostic-features extracted by convolu- tive neural networks (CNNs) (e.g., auto-encoders) [4]. The pattern recognition step will be based on Gaussian Mixture Model based classifiers,K-nearest neighbor classifiers, Bayes classifiers, as well as Deep Neural Networks. The Massachussets Eye and Ear Infirmary Dataset (MEEI-Dataset) [5] will be exploited. At the application level, a library for feature extraction and classification in Python will be developed. Credible publicly available resources will be 1used toward achieving our goal, such as KALDI. Comparisons will be made against [6-8].

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

A JavaScript Library for Dimensionality Reduction

A Julia package for manifold learning and nonlinear dimensionality reduction

a repository for my curriculum project

Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.

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

Showcasing Manifold Learning with ISOMAP, and compare the model to other transformations, such as PCA and MDS.

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

A comparison between some dimension reduction algorithms

Implementations of 3 linear and non-linear dimensionality reduction algorithms

The code for Multidimensional Scaling (MDS), Sammon Mapping, and Isomap.

Autoencoder model implementation in Keras, trained on MNIST dataset / latent space investigation.

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.

Variational Autoencoder

Implementations of MAP, Naive Bayes, PCA, MDS, ISOMAP and some compression

The main objective of this project is dimensionality reduction. We do dimensional reduction for reducing memory size and complexity of the model.

Simple ISOMAP and PCA decomposition algorithms

Applied Machine Learning (COMP 551) Course Project

No description provided.

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

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.

PYTHON PROGRAMMING

Unfolding the Swiss Roll Dataset explores different approaches to analyzing and visualizing the famous Swiss Roll dataset

Project to learn a bit more about dimensionality reduction techniques

Isomap is a data visualisation technique based on geodesic distance.

Optimal transport for comparing short brain connectivity between individuals | Optimal transport | Wasserstein distance | Barycenter | K-medoids | Isomap| Sulcus | Brain

Dimensionality reduction and data embedding via PCA, MDS, and Isomap.

Manifold mapping with ISOMAP (MATLAB).

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.