feature-extraction
Awesome papers on Feature Extraction (Dimensionality Reduction)
https://github.com/mlpapers/feature-extraction
Last synced: 6 days ago
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- CRAN
- Wiki
- Wiki
- sklearn
- Wiki
- Code
- Code
- A survey of dimensionality reduction techniques
- Feature Selection and Feature Extraction in Pattern Analysis: A Literature Review
- On lines and planes of closest fit to systems of points in space
- Prediction by Supervised Principal Components
- sklearn
- sklearn
- Nonlinear Component Analysis as a Kernel Eigenvalue Problem - Robert Muller*
- Kernel PCA for Novelty Detection
- Robust Kernel Principal Component Analysis
- CRAN - learn.org/stable/modules/generated/sklearn.decomposition.IncrementalPCA.html#sklearn.decomposition.IncrementalPCA))
- Wiki
- Independent Component Analysis - Free ebook *Aapo Hyvarinen, Juha Karhunen, Erkki Oja*
- Wiki
- The Utilization of Multiple Measurements in Problems of Biological Classification - require registration *C. Radhakrishna Rao*
- CRAN
- Wiki
- Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
- An Analysis of Classical Multidimensional Scaling
- Online Learning for Latent Dirichlet Allocation
- Wiki - learn.org/stable/modules/generated/sklearn.decomposition.FactorAnalysis.html#sklearn.decomposition.FactorAnalysis))
- Homepage - distributed_stochastic_neighbor_embedding), [CRAN](https://cran.r-project.org/web/packages/tsne/), [sklearn](https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html))
- Visualizing Data using t-SNE
- Accelerating t-SNE using Tree-Based Algorithms
- Code
- Tree-SNE: Hierarchical Clustering and Visualization Using t-SNE - Hoffman*
- Let-SNE: A Hybrid Approach to Data Embedding and Visualization of Hyperspectral Imagery
- UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
- Trimap: Large-scale Dimensionality Reduction Using Triplets
- Self-Organized Formation of Topologically Correct Feature Maps
- Visualizing Large-scale and High-dimensional Data
- Paper - project.org/web/packages/Rdimtools/))
- Prediction by Supervised Principal Components
- Nonlinear Component Analysis as a Kernel Eigenvalue Problem - Robert Muller*
- Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
- A survey of dimensionality reduction techniques
- On lines and planes of closest fit to systems of points in space
- Kernel PCA for Novelty Detection
- Robust Kernel Principal Component Analysis
- Independent Component Analysis: Algorithms and Applications
- The Use of Multiple Measurements in Taxonomic Problems
- PCA versus LDA
- An Analysis of Classical Multidimensional Scaling
- Homepage
- A Global Geometric Framework for Nonlinear Dimensionality Reduction
- Let-SNE: A Hybrid Approach to Data Embedding and Visualization of Hyperspectral Imagery
- Trimap: Large-scale Dimensionality Reduction Using Triplets
- Self-Organized Formation of Topologically Correct Feature Maps
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