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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 121–130 of 3304 papers

Title	Date	Tasks	Status	Hype
Kernelized Diffusion maps	Feb 13, 2023	ClusteringDimensionality Reduction	CodeCode Available	1
Large-scale optimal transport map estimation using projection pursuit	Jun 9, 2021	Dimensionality Reduction	CodeCode Available	1
An efficient aggregation method for the symbolic representation of temporal data	Jan 14, 2022	Dimensionality ReductionInformation Retrieval	CodeCode Available	1
A New Basis for Sparse Principal Component Analysis	Jul 1, 2020	ClusteringDimensionality Reduction	CodeCode Available	1
Learning-Based Dimensionality Reduction for Computing Compact and Effective Local Feature Descriptors	Sep 27, 2022	Dimensionality ReductionImage Retrieval	CodeCode Available	1
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)	Oct 1, 2021	Dimensionality ReductionGraph Representation Learning	CodeCode Available	1
A local approach to parameter space reduction for regression and classification tasks	Jul 22, 2021	ClusteringDimensionality Reduction	CodeCode Available	1
A Memory Efficient Baseline for Open Domain Question Answering	Dec 30, 2020	Dimensionality ReductionOpen-Domain Question Answering	CodeCode Available	1
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box Models	Jan 1, 2021	Dimensionality Reduction	CodeCode Available	1
Adversarial Autoencoders	Nov 18, 2015	ClusteringData Visualization	CodeCode Available	1

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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