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Dimensionality Reduction

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

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Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 3061–3070 of 3304 papers

Title	Date	Tasks	Status	Hype
SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel Learning	Mar 21, 2017	ClusteringDimensionality Reduction	CodeCode Available	0
Learning Integral Representations of Gaussian Processes	Feb 21, 2018	Dimensionality ReductionGaussian Processes	CodeCode Available	0
Random-projection ensemble dimension reduction	Oct 7, 2024	Dimensionality Reductionregression	CodeCode Available	0
Simple and Effective Dimensionality Reduction for Word Embeddings	Aug 11, 2017	Dimensionality ReductionInformation Retrieval	CodeCode Available	0
Features extraction and reduction techniques with optimized SVM for Persian/Arabic handwritten digits recognition	May 30, 2022	Bayesian OptimizationComputational Efficiency	CodeCode Available	0
Objective discovery of dominant dynamical processes with intelligible machine learning	Jun 21, 2021	BIG-bench Machine LearningDimensionality Reduction	CodeCode Available	0
Random Projection in Deep Neural Networks	Dec 22, 2018	Dimensionality Reduction	CodeCode Available	0
Featurizing Koopman Mode Decomposition For Robust Forecasting	Dec 14, 2023	Dimensionality Reduction	CodeCode Available	0
Whitening-Free Least-Squares Non-Gaussian Component Analysis	Mar 3, 2016	Dimensionality Reduction	CodeCode Available	0
Convergent autoencoder approximation of low bending and low distortion manifold embeddings	Aug 22, 2022	DecoderDimensionality Reduction	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