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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 61–70 of 3304 papers

Title	Date	Tasks	Status	Hype
Kernelized Diffusion maps	Feb 13, 2023	ClusteringDimensionality Reduction	CodeCode Available	1
Learning Topology-Preserving Data Representations	Jan 31, 2023	Dimensionality ReductionTriplet	CodeCode Available	1
Fast conformational clustering of extensive molecular dynamics simulation data	Jan 11, 2023	ClusteringDimensionality Reduction	CodeCode Available	1
Latent Autoregressive Source Separation	Jan 9, 2023	Dimensionality Reduction	CodeCode Available	1
Principal Component Analysis in Space Forms	Jan 6, 2023	Dimensionality ReductionForm	CodeCode Available	1
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image Generation	Dec 22, 2022	Dimensionality ReductionGenerative Adversarial Network	CodeCode Available	1
Quantifying Extrinsic Curvature in Neural Manifolds	Dec 20, 2022	Dimensionality ReductionTopological Data Analysis	CodeCode Available	1
Transform Once: Efficient Operator Learning in Frequency Domain	Nov 26, 2022	Dimensionality ReductionOperator learning	CodeCode Available	1
Efficient Feature Extraction for High-resolution Video Frame Interpolation	Nov 25, 2022	4kDimensionality Reduction	CodeCode Available	1
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size Data	Nov 25, 2022	Dimensionality Reductionfeature selection	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