SOTAVerified

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 2201–2225 of 3304 papers

TitleStatusHype
Improving the Projection of Global Structures in Data through Spanning Trees—0
A Light weight and Hybrid Deep Learning Model based Online Signature Verification—0
GP-VAE: Deep Probabilistic Time Series ImputationCode0
Visualization of Emergency Department Clinical Data for Interpretable Patient PhenotypingCode0
Generalized Principal Component Analysis—0
Learning GPLVM with arbitrary kernels using the unscented transformationCode0
Spectral Overlap and a Comparison of Parameter-Free, Dimensionality Reduction Quality MetricsCode0
Size matters for OTC market makers: general results and dimensionality reduction techniques—0
Obj-GloVe: Scene-Based Contextual Object Embedding—0
A data-driven approach for multiscale elliptic PDEs with random coefficients based on intrinsic dimension reduction—0
Understanding Memory Modules on Learning Simple Algorithms—0
Nature Inspired Dimensional Reduction Technique for Fast and Invariant Visual Feature Extraction—0
Neural Dynamics Discovery via Gaussian Process Recurrent Neural NetworksCode0
Anomaly Subsequence Detection with Dynamic Local Density for Time Series—0
An Improvement of PAA on Trend-Based Approximation for Time Series—0
Learning Fair Representations for Kernel ModelsCode0
Tuning-Free Disentanglement via Projection—0
Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images—0
Fisher and Kernel Fisher Discriminant Analysis: TutorialCode0
Screening Sinkhorn Algorithm for Regularized Optimal TransportCode0
Bayesian inverse regression for dimension reduction with small datasets—0
An unsupervised approach to Geographical Knowledge Discovery using street level and street network images—0
Estimating a Manifold from a Tangent Bundle Learner—0
Agriculture Commodity Arrival Prediction using Remote Sensing Data: Insights and Beyond—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—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