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 3031–3040 of 3304 papers

TitleStatusHype
Vector Space Models for Scientific Document Summarization—0
PRHLT: Combination of Deep Autoencoders with Classification and Regression Techniques for SemEval-2015 Task 11—0
USAAR-SHEFFIELD: Semantic Textual Similarity with Deep Regression and Machine Translation Evaluation Metrics—0
Non-Orthogonal Explicit Semantic Analysis—0
Projection Metric Learning on Grassmann Manifold With Application to Video Based Face Recognition—0
Heteroscedastic Max-Min Distance Analysis—0
Sufficient Forecasting Using Factor Models—0
Using Dimension Reduction to Improve the Classification of High-dimensional Data—0
Sequential Dimensionality Reduction for Extracting Localized Features—0
oASIS: Adaptive Column Sampling for Kernel Matrix Approximation—0
Show:102550
← PrevPage 304 of 331Next →
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