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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 181–190 of 3304 papers

Title	Date	Tasks	Status	Hype	Score
ActUp: Analyzing and Consolidating tSNE and UMAP	May 12, 2023	Dimensionality Reduction	CodeCode Available	1	5
BayesOpt Adversarial Attack	May 1, 2020	Adversarial AttackBayesian Optimisation	CodeCode Available	1	5
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection	Jul 26, 2024	Dimensionality ReductionDistributed Computing	CodeCode Available	1	5
Learning the dynamics of technical trading strategies	Mar 6, 2019	Dimensionality ReductionTime Series	CodeCode Available	1	5
Learning Wasserstein Embeddings	Oct 20, 2017	DecoderDimensionality Reduction	CodeCode Available	1	5
Less is more: Faster and better music version identification with embedding distillation	Oct 7, 2020	Dimensionality ReductionRetrieval	CodeCode Available	1	5
Linear Recursive Feature Machines provably recover low-rank matrices	Jan 9, 2024	Dimensionality ReductionLow-Rank Matrix Completion	CodeCode Available	1	5
Locally Linear Embedding and its Variants: Tutorial and Survey	Nov 22, 2020	Dimensionality ReductionSurvey	CodeCode Available	1	5
ManifoldNet: A Deep Network Framework for Manifold-valued Data	Sep 11, 2018	Dimensionality Reduction	CodeCode Available	1	5
DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning	Oct 4, 2023	Dimensionality ReductionIn-Context Learning	CodeCode Available	1	5

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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