SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 1281–1290 of 3304 papers

TitleStatusHype
Feature-aware Label Space Dimension Reduction for Multi-label Classification—0
Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study—0
Feature Engineering and Ensemble Modeling for Paper Acceptance Rank Prediction—0
Feature extraction using Latent Dirichlet Allocation and Neural Networks: A case study on movie synopses—0
Comparison of feature extraction and dimensionality reduction methods for single channel extracellular spike sorting—0
Feature Hashing for Language and Dialect Identification—0
Feature Hashing for Large Scale Multitask Learning—0
Feature Learning for Nonlinear Dimensionality Reduction toward Maximal Extraction of Hidden Patterns—0
Comparison of Methods in Skin Pigment Decomposition—0
An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification—0
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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