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 911–920 of 3304 papers

TitleStatusHype
Automatic Selection of t-SNE Perplexity—0
An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification—0
Automatic Prediction of the Performance of Every Parser—0
A Music Classification Model based on Metric Learning and Feature Extraction from MP3 Audio Files—0
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017—0
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation—0
3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
A Multi-view Dimensionality Reduction Algorithm Based on Smooth Representation Model—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