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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 2801–2810 of 3304 papers

Title	Date	Tasks	Status	Hype
Joint Embedding of Graphs	Mar 10, 2017	Dimensionality Reduction	CodeCode Available	0
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy	Mar 7, 2017	Dimensionality ReductionPrediction	—Unverified	0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction	Mar 6, 2017	Dimensionality ReductionFunctional Connectivity	—Unverified	0
Applying Ricci Flow to High Dimensional Manifold Learning	Mar 3, 2017	Dimensionality ReductionVocal Bursts Intensity Prediction	—Unverified	0
L^3-SVMs: Landmarks-based Linear Local Support Vectors Machines	Mar 1, 2017	Dimensionality Reduction	—Unverified	0
A description length approach to determining the number of k-means clusters	Feb 28, 2017	ClusteringData Compression	—Unverified	0
Semi-supervised Learning based on Distributionally Robust Optimization	Feb 28, 2017	Dimensionality Reduction	—Unverified	0
Feasibility of Principal Component Analysis in hand gesture recognition system	Feb 23, 2017	Dimensionality ReductionFace Detection	—Unverified	0
Visual response properties of MSTd emerge from a sparse population code	Feb 22, 2017	Dimensionality Reduction	—Unverified	0
Exemplar-Centered Supervised Shallow Parametric Data Embedding	Feb 21, 2017	Dimensionality ReductionGeneral Classification	—Unverified	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