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 1711–1720 of 3304 papers

TitleStatusHype
Analyzing movies to predict their commercial viability for producers—0
Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation—0
Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey—0
A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data—0
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box ModelsCode1
Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Graph Neural Network Acceleration via Matrix Dimension Reduction—0
Graph Learning via Spectral Densification—0
On the Importance of Distraction-Robust Representations for Robot Learning—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