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 101–125 of 3304 papers

TitleStatusHype
FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language ModelsCode1
Flows for simultaneous manifold learning and density estimationCode1
Correlation-based feature selection to identify functional dynamics in proteinsCode1
DartMinHash: Fast Sketching for Weighted SetsCode1
On Path Integration of Grid Cells: Group Representation and Isotropic ScalingCode1
Generative Locally Linear EmbeddingCode1
A Spectral Method for Assessing and Combining Multiple Data VisualizationsCode1
GiDR-DUN; Gradient Dimensionality Reduction -- Differences and UnificationCode1
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size DataCode1
Hartley Spectral Pooling for Deep LearningCode1
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breathCode1
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
Algorithmic Stability and Generalization of an Unsupervised Feature Selection AlgorithmCode1
Autoencoding with a Classifier SystemCode1
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image GenerationCode1
Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor ClassificationCode1
CBMAP: Clustering-based manifold approximation and projection for dimensionality reductionCode1
catch22: CAnonical Time-series CHaracteristicsCode1
Interpreting Temporal Graph Neural Networks with Koopman TheoryCode1
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality ReductionCode1
Kernelized Diffusion mapsCode1
Clustering with UMAP: Why and How Connectivity MattersCode1
BayesOpt Adversarial AttackCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
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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