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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3011–3020 of 3304 papers

Title	Date	Tasks	Status	Hype
Latent-space Physics: Towards Learning the Temporal Evolution of Fluid Flow	Feb 27, 2018	Dimensionality Reduction	CodeCode Available	0
A Bi-level Nonlinear Eigenvector Algorithm for Wasserstein Discriminant Analysis	Nov 21, 2022	Dimensionality Reduction	CodeCode Available	0
Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift	Oct 29, 2018	Dimensionality ReductionTwo-sample testing	CodeCode Available	0
Challenging Euclidean Topological Autoencoders	Oct 10, 2020	Dimensionality Reduction	CodeCode Available	0
Multi-Criteria Dimensionality Reduction with Applications to Fairness	Feb 28, 2019	Dimensionality ReductionFairness	CodeCode Available	0
Subspace Clustering through Sub-Clusters	Nov 15, 2018	ClusteringDimensionality Reduction	CodeCode Available	0
Lazy stochastic principal component analysis	Sep 21, 2017	Dimensionality Reduction	CodeCode Available	0
Challenges of Multi-Modal Coreset Selection for Depth Prediction	Feb 20, 2025	Depth EstimationDepth Prediction	CodeCode Available	0
Unsupervised Boosting-based Autoencoder Ensembles for Outlier Detection	Oct 22, 2019	Dimensionality ReductionDiversity	CodeCode Available	0
DeepFRC: An End-to-End Deep Learning Model for Functional Registration and Classification	Jan 30, 2025	Dimensionality ReductionHandwriting Recognition	CodeCode Available	0

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All datasets MCA EMNIST

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	UDRN	Classification Accuracy	90.9	—	Unverified
2	tSNE	Classification Accuracy	51.5	—	Unverified
3	IVIS	Classification Accuracy	46.6	—	Unverified
4	UMAP	Classification Accuracy	41.3	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UDRN	Classification Accuracy	71.1	—	Unverified
2	QS	Classification Accuracy	68	—	Unverified