SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2881–2890 of 3304 papers

TitleStatusHype
Capacity Analysis of Vector Symbolic Architectures—0
CA-PCA: Manifold Dimension Estimation, Adapted for Curvature—0
Capture Agent Free Biosensing using Porous Silicon Arrays and Machine Learning—0
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting—0
Cardiomyopathy Diagnosis Model from Endomyocardial Biopsy Specimens: Appropriate Feature Space and Class Boundary in Small Sample Size Data—0
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning—0
CASE -- Condition-Aware Sentence Embeddings for Conditional Semantic Textual Similarity Measurement—0
CASS: Cross Adversarial Source Separation via Autoencoder—0
Causal Deep Learning—0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
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All datasetsMCAEMNIST

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9—Unverified
2tSNEClassification Accuracy51.5—Unverified
3IVISClassification Accuracy46.6—Unverified
4UMAPClassification Accuracy41.3—Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1—Unverified
2QSClassification Accuracy68—Unverified