SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 2111–2120 of 3304 papers

TitleStatusHype
Spectral Non-Convex Optimization for Dimension Reduction with Hilbert-Schmidt Independence Criterion—0
Spectral, Probabilistic, and Deep Metric Learning: Tutorial and Survey—0
Spectral Representations for Convolutional Neural Networks—0
Spectral Self-supervised Feature Selection—0
Spectral Sparse Representation for Clustering: Evolved from PCA, K-means, Laplacian Eigenmap, and Ratio Cut—0
Speech Emotion Recognition Using Deep Sparse Auto-Encoder Extreme Learning Machine with a New Weighting Scheme and Spectro-Temporal Features Along with Classical Feature Selection and A New Quantum-Inspired Dimension Reduction Method—0
Spherical Principal Curves—0
Spike and slab Bayesian sparse principal component analysis—0
Spike and Slab Gaussian Process Latent Variable Models—0
SPreV—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