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 681–690 of 3304 papers

TitleStatusHype
Bounds on Representation-Induced Confounding Bias for Treatment Effect EstimationCode0
Bit Cipher -- A Simple yet Powerful Word Representation System that Integrates Efficiently with Language Models—0
An Improved CNN-based Neural Network Model for Fruit Sugar Level Detection—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL—0
Handling Overlapping Asymmetric Datasets -- A Twice Penalized P-Spline Approach—0
Finding Real-World Orbital Motion Laws from Data—0
From Pretext to Purpose: Batch-Adaptive Self-Supervised Learning—0
Simple but Effective Unsupervised Classification for Specified Domain Images: A Case Study on Fungi Images—0
The optimal resolution level of a protein is an emergent property of its structure and dynamicsCode0
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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