data science

Paper

tSNE

Better Local Structure, finer structure

Heavy-tailed kernels reveal a finer cluster structure in t-SNE visualisations

https://arxiv.org/abs/1902.05804

UMAP

Better Global Structure

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

https://arxiv.org/abs/1802.03426

Centrality

Survey on Degree, Closeness, Betweenness, PageRank, Coreness

https://arxiv.org/abs/2011.07190

Useful link

Roadmap

Supervised

1. Regression: Linear, Ridge, Lasso, Logistic, non-linear
2. Spatial Partition: kNN, decision tree, ReLu diagram
3. Random Forest
4. SVM: soft/hard, kernelized
5. NNs

Unsupervised

1. Dimensionality reduction: PCA, SVD, NMF, Wavelet Decomposition
2. Clustering: k-means, Hierarchical Clustering, Silhouette Score
3. Manifold Learning:

Network

1. Spectrum Decomposition
2. Centrality: PageRank, Degree, Eigenvector
3. Community Detection: Louvain-algo, Adjust Rank Index(ARI)
4. Maximum Subgraph