SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2721–2730 of 3304 papers

TitleStatusHype
Visualizing and Exploring Dynamic High-Dimensional Datasets with LION-tSNE—0
ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute ModelsCode0
Multi-Stage Feature Selection Based Intelligent Classifier for Classification of Incipient Stage Fire in Building—0
Simple and Effective Dimensionality Reduction for Word EmbeddingsCode0
Automatic Selection of t-SNE Perplexity—0
How Do People Differ? A Social Media Approach—0
A Bootstrap Method for Error Estimation in Randomized Matrix Multiplication—0
A Latent Variable Model for Two-Dimensional Canonical Correlation Analysis and its Variational Inference—0
How Close Are the Eigenvectors of the Sample and Actual Covariance Matrices?—0
Parameter Free Hierarchical Graph-Based Clustering for Analyzing Continuous Word Embeddings—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