GitHub topics: isomap
moocf/isomap.python
Isomap is a data visualisation technique based on geodesic distance.
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N1k1f0rM/gyro-cluster
Clustering human activity by accelerometer from smartphone
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saehm/DruidJS
A JavaScript Library for Dimensionality Reduction
Language: JavaScript - Size: 6.18 MB - Last synced at: 3 days ago - Pushed at: 7 months ago - Stars: 116 - Forks: 10
dsrichard97/STAT-576-Phishing-Final-Project Fork of CorySuzuki1729/STAT-576-Phishing-Final-Project
A final project authored by Cory Suzuki, Nathaniel Talampas, and Richard Diaz DeLeon for Dr. Seungjoon Lee's Unsupervised Learning class. Here we perform dimensionality reduction techniques for feature extraction and utilize clustering methods to analyze insightful trends on the classification of phishing and scam emails.
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fratorgano/dimensionality-reduction
Project to learn a bit more about dimensionality reduction techniques
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matteo-serafino/dimensionality-reduction-package
Python package for plug and play dimensionality reduction techniques and data visualization in 2D or 3D.
Language: Python - Size: 36.1 KB - Last synced at: 24 days ago - Pushed at: 7 months ago - Stars: 4 - Forks: 0
drewwilimitis/Manifold-Learning
Introduction to Manifold Learning - Mathematical Theory and Applied Python Examples (Multidimensional Scaling, Isomap, Locally Linear Embedding, Spectral Embedding/Laplacian Eigenmaps)
Language: Jupyter Notebook - Size: 140 MB - Last synced at: 5 months ago - Pushed at: about 5 years ago - Stars: 222 - Forks: 37
wildart/ManifoldLearning.jl
A Julia package for manifold learning and nonlinear dimensionality reduction
Language: Julia - Size: 3.5 MB - Last synced at: 13 days ago - Pushed at: about 1 year ago - Stars: 92 - Forks: 22
catherman/Advanced-Analytics-and-Statistical-Concepts
Data analysis using Principal Component Analysis (PCA), Eigenvalues, Covariance matrix, Maximum Likelihood Estimation (MLE), ISOMAP, & Image recognition
Language: Python - Size: 9.33 MB - Last synced at: 10 months ago - Pushed at: 10 months ago - Stars: 0 - Forks: 0
MuzzyB/Exploring-Cybersecurity-Data-Science
Exploring Cybersecurity Data Science: Dimensionality Reduction and Cluster Analysis
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aperiodik/Macrophenological-dynamics-paper
data and R code to reproduce the analysis and plots presented in the manuscript: "Macrophenological dynamics from citizen science plant occurrence data"
Language: R - Size: 41.6 MB - Last synced at: 11 months ago - Pushed at: 11 months ago - Stars: 0 - Forks: 1
GioStamoulos/Kmers_Dataset_Generation_Regression_Clustering
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.
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nikapotato/dimensionality-reduction
The key dimensionality reduction techniques: ISOMAP, PCA (Principal Component Analysis), and t-SNE (t-Distributed Stochastic Neighbor Embedding) are presented and compared.
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alessimichele/Unsupervised-Learning-2023 Fork of ilariavascotto/Unsupervised-Learning-2023
This repository is dedicated to the lab activities of the course of Unsupervised Learning @UniTs
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jgurakuqi/graph-kernels-and-manifold-svm
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.
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bghojogh/MDS-SammonMapping-Isomap
The code for Multidimensional Scaling (MDS), Sammon Mapping, and Isomap.
Language: Python - Size: 89.2 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 2
jonzia/Manifold
Manifold mapping with ISOMAP (MATLAB).
Language: MATLAB - Size: 12.7 KB - Last synced at: over 1 year ago - Pushed at: about 4 years ago - Stars: 1 - Forks: 0
gionanide/Speech_Signal_Processing_and_Classification
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].
Language: Python - Size: 827 KB - Last synced at: over 1 year ago - Pushed at: about 2 years ago - Stars: 220 - Forks: 62
Sagarnandeshwar/Visualizing_High_Dimensional_Data
Applied Machine Learning (COMP 551) Course Project
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fsarab/MDS-ISOMAP
This project includes implementations of the MDS and ISOMAP algorithms using Python and various libraries such as NumPy, Matplotlib, Scikit-learn, and NetworkX.
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MJAHMADEE/VAE
Variational Autoencoder
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Sarvandani/Machine_learning_6_algorithms_of_dimensionality_reduction
Sklearn, PCA, t-SNE, Isomap, NMF, Random Projection, Spectral Embedding
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Smendowski/data-embedding-and-visualization
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.
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lowhung/naive-bayes-pca-mds
Implementations of MAP, Naive Bayes, PCA, MDS, ISOMAP and some compression
Language: Python - Size: 1.82 MB - Last synced at: 4 months ago - Pushed at: over 7 years ago - Stars: 3 - Forks: 0
chris-santiago/decomposition
Simple ISOMAP and PCA decomposition algorithms
Language: Python - Size: 7.81 KB - Last synced at: 4 days ago - Pushed at: almost 5 years ago - Stars: 2 - Forks: 0
jasonfilippou/DimReduce
Implementations of 3 linear and non-linear dimensionality reduction algorithms
Language: Python - Size: 48.3 MB - Last synced at: over 1 year ago - Pushed at: over 4 years ago - Stars: 3 - Forks: 1
mpolinowski/isometric-mapping
Non-linear dimensionality reduction through Isometric Mapping
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mpolinowski/manifold-learning-for-image-segmentation
Use Manifold Learning, Mapping and Discriminant Analysis to Visualize Image Datasets
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svachmic-ctu/isomap
Example implementation of Isomap algorithm in R
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AAU-Dat/P5-Nonlinear-Dimensionality-Reduction
5th semester project concerning feature engineering and nonlinear dimensionality reduction in particular.
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tracy-talent/curriculum
a repository for my curriculum project
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KodAgge/AdvancedMachineLearning
A collection of the assignments in the course advanced machine learning
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PyDimRed/PyDimRed
A comparison between some dimension reduction algorithms
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tejasnp163/Dimensionality-Reduction-on-Wine-Dataset
Performed different tasks such as data preprocessing, cleaning, classification, and feature extraction/reduction on wine dataset.
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tate8/dimensionality-reduction
Performing dimensionality reduction with various ML algorithms
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Pradnya1208/Dimensionality-Reduction-Techniques
The goal of this project is to understand and build various dimensionality reduction techniques.
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Daphilippe/brain_connectivity
Optimal transport for comparing short brain connectivity between individuals | Optimal transport | Wasserstein distance | Barycenter | K-medoids | Isomap| Sulcus | Brain
Language: Python - Size: 126 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 0
mark-antal-csizmadia/pca-mds-isomap
Dimensionality reduction and data embedding via PCA, MDS, and Isomap.
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Arijit1000/ISOMAP-implementation
The main objective of this project is dimensionality reduction. We do dimensional reduction for reducing memory size and complexity of the model.
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Oliver-Binns/MLAP
Open Assessment for Machine Learning and Applications module. This assessment scored 83% and was worth 8 credits of my third year.
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vashistak/dimensionality-reduction-techniques
PYTHON PROGRAMMING
Language: Python - Size: 13.1 MB - Last synced at: about 2 years ago - Pushed at: about 8 years ago - Stars: 0 - Forks: 1
