SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2081–2090 of 3304 papers

TitleStatusHype
Safe squeezing for antisparse codingCode0
The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
Topological Stability: a New Algorithm for Selecting The Nearest Neighbors in Non-Linear Dimensionality Reduction Techniques—0
Efficient Fair Principal Component Analysis—0
Deep Generative Models Strike Back! Improving Understanding and Evaluation in Light of Unmet Expectations for OoD Data—0
Collaborative Homomorphic Computation on Data Encrypted under Multiple Keys—0
Manifold Denoising by Nonlinear Robust Principal Component AnalysisCode0
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity—0
Decoding Imagined Speech and Computer Control using Brain Waves—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