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 2126–2150 of 3304 papers

TitleStatusHype
Stable Sparse Subspace Embedding for Dimensionality Reduction—0
Static and Dynamic Robust PCA and Matrix Completion: A Review—0
Statistical Advantages of Oblique Randomized Decision Trees and Forests—0
Statistical and Computational Guarantees of Kernel Max-Sliced Wasserstein Distances—0
Statistical and computational trade-offs in estimation of sparse principal components—0
Statistical guarantees for generative models without domination—0
Statistical physics, Bayesian inference and neural information processing—0
Statistical tuning of artificial neural network—0
STDP enables spiking neurons to detect hidden causes of their inputs—0
Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction—0
Stochastic Approximation Algorithms for Principal Component Analysis—0
Stochastic Approximation for Online Tensorial Independent Component Analysis—0
Stochastic Bottleneck: Rateless Auto-Encoder for Flexible Dimensionality Reduction—0
Performance-Complexity Tradeoffs in Greedy Weak Submodular Maximization with Random Sampling—0
Stochastic Linear Bandits with Hidden Low Rank Structure—0
Stochastic Multidimensional Scaling—0
Stochastic Neighbor Embedding—0
Stochastic Neighbor Embedding separates well-separated clusters—0
Stochastic variance reduced multiplicative update for nonnegative matrix factorization—0
Stock Price Prediction using Principle Components—0
Strategy for Rapid Diabetic Retinopathy Exposure Based on Enhanced Feature Extraction Processing—0
Stratospheric aerosol source inversion: Noise, variability, and uncertainty quantification—0
Strong and weak principles of neural dimension reduction—0
Structural Learning and Integrative Decomposition of Multi-View Data—0
Structured adaptive and random spinners for fast machine learning computations—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