Dimensionality reduction and data embedding via PCA, MDS, and Isomap.
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Updated
Aug 1, 2021 - Jupyter Notebook
Dimensionality reduction and data embedding via PCA, MDS, 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.
Project to learn a bit more about dimensionality reduction techniques
The main objective of this project is dimensionality reduction. We do dimensional reduction for reducing memory size and complexity of the model.
Isomap is a data visualisation technique based on geodesic distance.
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
Exploring Cybersecurity Data Science: Dimensionality Reduction and Cluster Analysis
Open Assessment for Machine Learning and Applications module. This assessment scored 83% and was worth 8 credits of my third year.
Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.
Applied Machine Learning (COMP 551) Course Project
Implementations of MAP, Naive Bayes, PCA, MDS, ISOMAP and some compression
Variational Autoencoder
Simple ISOMAP and PCA decomposition algorithms
The key dimensionality reduction techniques: ISOMAP, PCA (Principal Component Analysis), and t-SNE (t-Distributed Stochastic Neighbor Embedding) are presented and compared.
Optimal transport for comparing short brain connectivity between individuals | Optimal transport | Wasserstein distance | Barycenter | K-medoids | Isomap| Sulcus | Brain
My assignments for homework of Computational Data Mining course at Amirkabir University of Technology
Messing around with isometric rendering of tilemaps
Showcasing Manifold Learning with ISOMAP, and compare the model to other transformations, such as PCA and MDS.
Manifold mapping with ISOMAP (MATLAB).
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