a repository for my curriculum project
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…
Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
A Julia package for manifold learning and nonlinear dimensionality reduction
A JavaScript Library for Dimensionality Reduction
5th semester project concerning feature engineering and nonlinear dimensionality reduction in particular.
The code for Multidimensional Scaling (MDS), Sammon Mapping, and Isomap.
A comparison between some dimension reduction algorithms
The goal of this project is to understand and build various dimensionality reduction techniques.
Implementations of 3 linear and non-linear dimensionality reduction algorithms
data and R code to reproduce the analysis and plots presented in the manuscript: "Macrophenological dynamics from citizen science plant occurrence data"
Example implementation of Isomap algorithm in R
Variational Autoencoder
Implementations of MAP, Naive Bayes, PCA, MDS, ISOMAP and some compression
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.
The main objective of this project is dimensionality reduction. We do dimensional reduction for reducing memory size and complexity of the model.
My assignments for homework of Computational Data Mining course at Amirkabir University of Technology
This project includes implementations of the MDS and ISOMAP algorithms using Python and various libraries such as NumPy, Matplotlib, Scikit-learn, and NetworkX.
Dimensionality reduction and data embedding via PCA, MDS, and Isomap.
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