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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 2231–2240 of 3304 papers

Title	Date	Tasks	Status	Hype
Learning Clustered Representation for Complex Free Energy Landscapes	Jun 7, 2019	ClusteringDimensionality Reduction	—Unverified	0
Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers	Jun 6, 2019	AllDense Video Captioning	—Unverified	0
Multi-Frequency Vector Diffusion Maps	Jun 6, 2019	Dimensionality Reduction	—Unverified	0
Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence	Jun 4, 2019	Autonomous VehiclesDimensionality Reduction	CodeCode Available	0
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission	Jun 3, 2019	Content-Based Image RetrievalDimensionality Reduction	—Unverified	0
Unsupervised and Supervised Principal Component Analysis: Tutorial	Jun 1, 2019	Dimensionality Reduction	CodeCode Available	0
Linear and Quadratic Discriminant Analysis: Tutorial	Jun 1, 2019	Dimensionality ReductionGeneral Classification	CodeCode Available	0
A Music Classification Model based on Metric Learning and Feature Extraction from MP3 Audio Files	May 30, 2019	Dimensionality ReductionGeneral Classification	—Unverified	0
The spiked matrix model with generative priors	May 29, 2019	Dimensionality Reductionmodel	CodeCode Available	0
Adapting Text Embeddings for Causal Inference	May 29, 2019	Causal IdentificationCausal Inference	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