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 2751–2775 of 3304 papers

TitleStatusHype
An Information Theory-Based Feature Selection Framework for Big Data Under Apache SparkCode0
Kernel Feature Selection via Conditional Covariance MinimizationCode0
Kernel Scaling for Manifold Learning and Classification—0
Vectorial Dimension Reduction for Tensors Based on Bayesian Inference—0
Dimensionality reduction with missing values imputation—0
Skip-Gram − Zipf + Uniform = Vector Additivity—0
Additive Component Analysis—0
Feature Hashing for Language and Dialect Identification—0
FFTLasso: Large-Scale LASSO in the Fourier Domain—0
Grassmannian Manifold Optimization Assisted Sparse Spectral Clustering—0
Designing Illuminant Spectral Power Distributions for Surface ClassificationCode0
Generalized notions of sparsity and restricted isometry property. Part I: A unified framework—0
CatBoost: unbiased boosting with categorical featuresCode0
Efficient Manifold and Subspace Approximations with SphereletsCode0
Improving text classification with vectors of reduced precisionCode0
Dimensionality Reduction using Similarity-induced EmbeddingsCode0
Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network—0
Hidden Talents of the Variational AutoencoderCode0
A survey of dimensionality reduction techniques based on random projection—0
Embedding Feature Selection for Large-scale Hierarchical Classification—0
Multi-View Kernels for Low-Dimensional Modeling of Seismic Events—0
Dimensionality reduction for acoustic vehicle classification with spectral embedding—0
Segmented and Non-Segmented Stacked Denoising Autoencoder for Hyperspectral Band Reduction—0
Linear Dimensionality Reduction in Linear Time: Johnson-Lindenstrauss-type Guarantees for Random Subspace—0
Maximum Margin Principal Components—0
Show:102550
← PrevPage 111 of 133Next →
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