SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 26–50 of 3304 papers

TitleStatusHype
Curvature-based Feature Selection with Application in Classifying Electronic Health RecordsCode1
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
Deep active subspaces - a scalable method for high-dimensional uncertainty propagationCode1
Deep Dimension Reduction for Supervised Representation LearningCode1
Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking SystemsCode1
The Signature Kernel is the solution of a Goursat PDECode1
Effective Sample Size, Dimensionality, and Generalization in Covariate Shift AdaptationCode1
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
Deep reconstruction of strange attractors from time seriesCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
BayesOpt Adversarial AttackCode1
CatBoost: gradient boosting with categorical features supportCode1
Algorithmic Stability and Generalization of an Unsupervised Feature Selection AlgorithmCode1
Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor ClassificationCode1
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breathCode1
ActUp: Analyzing and Consolidating tSNE and UMAPCode1
Autoencoding with a Classifier SystemCode1
BasisVAE: Translation-invariant feature-level clustering with Variational AutoencodersCode1
Bayesian Optimization of Sampling Densities in MRICode1
Adversarial AutoencodersCode1
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
Clustering with UMAP: Why and How Connectivity MattersCode1
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional DatasetsCode1
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reductionCode1
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
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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