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 1101–1125 of 3304 papers

TitleStatusHype
Efficient Sparse PCA via Block-Diagonalization—0
Efficient Tensor Contraction via Fast Count Sketch—0
Efficient Text Classification Using Tree-structured Multi-linear Principal Component Analysis—0
Eigen-GNN: A Graph Structure Preserving Plug-in for GNNs—0
EigenGS Representation: From Eigenspace to Gaussian Image Space—0
ELA: Efficient Local Attention for Deep Convolutional Neural Networks—0
Elastic deep autoencoder for text embedding clustering by an improved graph regularization—0
Electrical Grid Anomaly Detection via Tensor Decomposition—0
Elementos da teoria de aprendizagem de máquina supervisionada—0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
Can the Problem-Solving Benefits of Quality Diversity Be Obtained Without Explicit Diversity Maintenance?—0
Elliptical modeling and pattern analysis for perturbation models and classfication—0
Embedding-Assisted Attentional Deep Learning for Real-World RF Fingerprinting of Bluetooth—0
Embedding Compression for Efficient Re-Identification—0
Embedding Feature Selection for Large-scale Hierarchical Classification—0
Embedding Functional Data: Multidimensional Scaling and Manifold Learning—0
Embedding Hard Physical Constraints in Convolutional Neural Networks for 3D Turbulence—0
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature—0
EMC2A-Net: An Efficient Multibranch Cross-channel Attention Network for SAR Target Classification—0
Emotional Dimension Control in Language Model-Based Text-to-Speech: Spanning a Broad Spectrum of Human Emotions—0
Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods—0
Empirical comparison between autoencoders and traditional dimensionality reduction methods—0
Empirical Evaluation of Kernel PCA Approximation Methods in Classification Tasks—0
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting—0
Entangled Kernels -- Beyond Separability—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