topometryA comprehensive dimensional reduction framework to recover the latent topology from high-dimensional data.
Stars: ✭ 64 (+120.69%)
Mutual labels: dimensionality-reduction, single-cell-analysis
ezancestryEasy genetic ancestry predictions in Python
Stars: ✭ 38 (+31.03%)
Mutual labels: dimensionality-reduction
NIDS-Intrusion-DetectionSimple Implementation of Network Intrusion Detection System. KddCup'99 Data set is used for this project. kdd_cup_10_percent is used for training test. correct set is used for test. PCA is used for dimension reduction. SVM and KNN supervised algorithms are the classification algorithms of project. Accuracy : %83.5 For SVM , %80 For KNN
Stars: ✭ 45 (+55.17%)
Mutual labels: dimensionality-reduction
Awesome Single CellCommunity-curated list of software packages and data resources for single-cell, including RNA-seq, ATAC-seq, etc.
Stars: ✭ 1,937 (+6579.31%)
Mutual labels: dimensionality-reduction
Unsupervised-Learning-in-RWorkshop (6 hours): Clustering (Hdbscan, LCA, Hopach), dimension reduction (UMAP, GLRM), and anomaly detection (isolation forests).
Stars: ✭ 34 (+17.24%)
Mutual labels: dimensionality-reduction
Data-ScienceUsing Kaggle Data and Real World Data for Data Science and prediction in Python, R, Excel, Power BI, and Tableau.
Stars: ✭ 15 (-48.28%)
Mutual labels: dimensionality-reduction
UMAP.jlUniform Manifold Approximation and Projection (UMAP) implementation in Julia
Stars: ✭ 93 (+220.69%)
Mutual labels: dimensionality-reduction
partitionA fast and flexible framework for data reduction in R
Stars: ✭ 33 (+13.79%)
Mutual labels: dimensionality-reduction
snATAC<<------ Use SnapATAC!!
Stars: ✭ 23 (-20.69%)
Mutual labels: single-cell-analysis
Awesome Community DetectionA curated list of community detection research papers with implementations.
Stars: ✭ 1,874 (+6362.07%)
Mutual labels: dimensionality-reduction
UmapUniform Manifold Approximation and Projection
Stars: ✭ 5,268 (+18065.52%)
Mutual labels: dimensionality-reduction
tGPLVMtGPLVM: A Nonparametric, Generative Model for Manifold Learning with scRNA-seq experimental data
Stars: ✭ 16 (-44.83%)
Mutual labels: dimensionality-reduction
twpca🕝 Time-warped principal components analysis (twPCA)
Stars: ✭ 118 (+306.9%)
Mutual labels: dimensionality-reduction
dbMAPA fast, accurate, and modularized dimensionality reduction approach based on diffusion harmonics and graph layouts. Escalates to millions of samples on a personal laptop. Adds high-dimensional big data intrinsic structure to your clustering and data visualization workflow.
Stars: ✭ 39 (+34.48%)
Mutual labels: dimensionality-reduction
NebulosaR package to visualize gene expression data based on weighted kernel density estimation
Stars: ✭ 50 (+72.41%)
Mutual labels: single-cell-analysis
dmlR package for Distance Metric Learning
Stars: ✭ 58 (+100%)
Mutual labels: dimensionality-reduction
HARRecognize one of six human activities such as standing, sitting, and walking using a Softmax Classifier trained on mobile phone sensor data.
Stars: ✭ 18 (-37.93%)
Mutual labels: dimensionality-reduction
Machine Learning With PythonPractice and tutorial-style notebooks covering wide variety of machine learning techniques
Stars: ✭ 2,197 (+7475.86%)
Mutual labels: dimensionality-reduction
sefA Python Library for Similarity-based Dimensionality Reduction
Stars: ✭ 24 (-17.24%)
Mutual labels: dimensionality-reduction
mathematics-statistics-for-data-scienceMathematical & Statistical topics to perform statistical analysis and tests; Linear Regression, Probability Theory, Monte Carlo Simulation, Statistical Sampling, Bootstrapping, Dimensionality reduction techniques (PCA, FA, CCA), Imputation techniques, Statistical Tests (Kolmogorov Smirnov), Robust Estimators (FastMCD) and more in Python and R.
Stars: ✭ 56 (+93.1%)
Mutual labels: dimensionality-reduction
