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Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2051–2075 of 3304 papers

TitleStatusHype
Simultaneously Learning Neighborship and Projection Matrix for Supervised Dimensionality Reduction—0
Simultaneous Spectral-Spatial Feature Selection and Extraction for Hyperspectral Images—0
Single-Pass PCA of Large High-Dimensional Data—0
Single-Sequence-Based Protein Secondary Structure Prediction using One-Hot and Chemical Encodings of Amino Acids—0
S-Isomap++: Multi Manifold Learning from Streaming Data—0
Size matters for OTC market makers: general results and dimensionality reduction techniques—0
Sketched Subspace Clustering—0
Skip-Gram − Zipf + Uniform = Vector Additivity—0
SKYNET: an efficient and robust neural network training tool for machine learning in astronomy—0
Small-data Reduced Order Modeling of Chaotic Dynamics through SyCo-AE: Synthetically Constrained Autoencoders—0
SMAP: A Joint Dimensionality Reduction Scheme for Secure Multi-Party Visualization—0
Smart Analytical Signature Verification For DSP Applications—0
Smile and Laugh Expressions Detection Based on Local Minimum Key Points—0
Smoothing Nonlinear Variational Objectives with Sequential Monte Carlo—0
Soft Locality Preserving Map (SLPM) for Facial Expression Recognition—0
Solve sparse PCA problem by employing Hamiltonian system and leapfrog method—0
Solving Interpretable Kernel Dimensionality Reduction—0
Some Options for L1-Subspace Signal Processing—0
SpaceEditing: Integrating Human Knowledge into Deep Neural Networks via Interactive Latent Space Editing—0
Space-Filling Curve Indices as Acceleration Structure for Exemplar-Based Inpainting—0
Spaceland Embedding of Sparse Stochastic Graphs—0
SpaceMAP: Visualizing Any Data in 2-dimension by Space Expansion—0
Space-Time Extension of the MEM Approach for Electromagnetic Neuroimaging—0
Space-Time Local Embeddings—0
Sparse Centroid-Encoder: A Nonlinear Model for Feature Selection—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified