Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
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Updated
Mar 5, 2020 - Jupyter Notebook
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…
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.
A comparison between some dimension reduction algorithms
Python package for plug and play dimensionality reduction techniques and data visualization in 2D or 3D.
Implementations of 3 linear and non-linear dimensionality reduction algorithms
Autoencoder model implementation in Keras, trained on MNIST dataset / latent space investigation.
The code for Multidimensional Scaling (MDS), Sammon Mapping, and Isomap.
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.
Implementations of MAP, Naive Bayes, PCA, MDS, ISOMAP and some compression
Variational Autoencoder
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
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