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dimensionality-reduction-technique

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michelin / TorchSOM

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TorchSOM is a PyTorch-based library for training Self-Organizing Maps (SOMs), a type of unsupervised learning algorithm used for clustering and dimensionality reduction. Designed for scalability and ease of use, this implementation is optimized for large-scale data.

clustering pytorch data-visualisation clustering-algorithm unsupervised-machine-learning pytorch-implementation just-in-time self-organizing-maps dimensionality-reduction-technique dimensionality-reduction-algorithms just-in-time-learning
  • Updated Aug 20, 2025
  • Jupyter Notebook

Ayantika22 / PCA-Principle-Component-Analysis-For-Seed-Dataset

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PCA(Principle Component Analysis) For Seed Dataset in Machine Learning

classifier machine-learning jupyter-notebook classification accuracy logistic-regression python-3 unsupervised-learning support-vector-machines decision-tree-classifier principle-component-analysis random-forest-classifier knn-classifier gaussian-naive-bayes seed-dataset k-nearest-neighbor-classifier dimensionality-reduction-technique cohen-kappa
  • Updated Jun 30, 2020
  • Jupyter Notebook

Ayantika22 / LDA-Linear-Discriminant-Analysis-for-Seed-Dataset

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LDA(Linear Discriminant Analysis) for Seed Dataset

classifier machine-learning classification accuracy python-3 support-vector-machine jupyter-notebooks unsupervised-learning decision-tree-classifier random-forest-classifier k-nearest-neighbors knn-classifier linear-discriminant-analysis-lda gaussian-naive-bayes seed-dataset logistic-regression-classifier dimensionality-reduction-technique cohen-kappa
  • Updated Jun 30, 2020
  • Jupyter Notebook

rajvi-patel-22 / Linear-Discriminant-Analysis-of-a-32x32-grayscale-image-for-image-compression

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Discriminant analysis methods can be good candidates to address such problems. These methods are supervised, so they include label information. The goal is to find directions on which the data is best separable. One of the very wellknown discriminant analysis method is the Linear Discriminant Analysis. Linear Discriminant Analysis (LDA) is most …

variance linear-discriminant-analysis signals-and-systems computer-organisation linear-algebra-concepts wellknown-discriminant-analysis discriminant-analysis-methods dimensionality-reduction-technique lda-technique la-ss-co-project
  • Updated Dec 31, 2019
different_processings_for_ML

Jean-Lcs / different_processings_for_ML

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In this project, we use differents methods to transform our dataset (usually dimension modification) before making prediction thanks to machine learning and regressions.

variable-selection dimensional-analysis pca-analysis pca dimensionality-reduction ridge-regression variables principal-component-analysis ols-regression principal-components ridge regularisation principal-component-analysis-pca dimensionality-reduction-technique regularisation-methods
  • Updated Feb 28, 2022
  • Jupyter Notebook

satishmekkonda / DataScience-Projects

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This repository consists of 6 sections, detailing hands on Machine Learning Models: Regression, Classification, Clustering, AssocaitionRuleLearning, Deep Learning and Natural Language Processing Techniques

natural-language-processing reinforcement-learning neural-networks association-rules regression-models classification-methods clustering-methods xgboost-model kfold-cross-validation gridsearchcv dimensionality-reduction-technique
  • Updated May 10, 2020
  • Python

mohammad95labbaf / UMAP_breast_cancer

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This repository explores the interplay between dimensionality reduction techniques and classification algorithms in the realm of breast cancer diagnosis. Leveraging the Breast Cancer Wisconsin dataset, it assesses the impact of various methods, including PCA, Kernel PCA, LLE, UMAP, and Supervised UMAP, on the performance of a Decision Tree.

eda dimensionality-reduction lle decision-tree principal-component-analysis decision-tree-classifier breast-cancer-wisconsin umap kernel-pca locally-linear-embedding dimensionality-reduction-technique expolatory-data-analysis
  • Updated Jan 26, 2024
  • Jupyter Notebook

tanyakuznetsova / Multidimensional-Scaling-of-European_Cities

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This project explores the spatial relationships between twenty European cities using classical manual Multidimensional Scaling (MDS), MDS from scikit-learn, and compares the results with Principal Component Analysis (PCA).

pca dimensionality-reduction mds unsupervised-learning principal-component-analysis multidimensional-scaling unsupervised-machine-learning dimensionality-reduction-technique classical-ml-algorithms
  • Updated Jan 8, 2024
  • Jupyter Notebook

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