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 601–625 of 3304 papers

TitleStatusHype
An Introduction to Autoencoders—0
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication—0
Cone-Constrained Principal Component Analysis—0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
Clustering small datasets in high-dimension by random projection—0
An Innovative Imputation and Classification Approach for Accurate Disease Prediction—0
A Fuzzy Approach for Feature Evaluation and Dimensionality Reduction to Improve the Quality of Web Usage Mining Results—0
Distance preserving model order reduction of graph-Laplacians and cluster analysis—0
A Functional approach for Two Way Dimension Reduction in Time Series—0
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics—0
Clustering high dimensional meteorological scenarios: results and performance index—0
Clustering, Classification, Discriminant Analysis, and Dimension Reduction via Generalized Hyperbolic Mixtures—0
Cluster Weighted Model Based on TSNE algorithm for High-Dimensional Data—0
ClusTop: An unsupervised and integrated text clustering and topic extraction framework—0
C(NN)FD -- Deep Learning Modelling of Multi-Stage Axial Compressors Aerodynamics—0
Deep learning modelling of manufacturing and build variations on multi-stage axial compressors aerodynamics—0
An Investigation of Newton-Sketch and Subsampled Newton Methods—0
Cognitive Coding of Speech—0
Collaborative causal inference on distributed data—0
Collaborative Gaussian Processes for Preference Learning—0
Collaborative Homomorphic Computation on Data Encrypted under Multiple Keys—0
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach—0
Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
Conditional Density Estimation with Dimensionality Reduction via Squared-Loss Conditional Entropy Minimization—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