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 1751–1760 of 3304 papers

TitleStatusHype
Parallel Transport Unfolding: A Connection-based Manifold Learning Approach—0
Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings—0
Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach—0
Parameter Optimization using high-dimensional Bayesian Optimization—0
Parametric UMAP: learning embeddings with deep neural networks for representation and semi-supervised learning—0
Parametrization of stochastic inputs using generative adversarial networks with application in geology—0
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals—0
PCA-Based Out-of-Sample Extension for Dimensionality Reduction—0
PCA-Boosted Autoencoders for Nonlinear Dimensionality Reduction in Low Data Regimes—0
PCA-RAG: Principal Component Analysis for Efficient Retrieval-Augmented Generation—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