SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2776–2800 of 3304 papers

TitleStatusHype
A survey of dimensionality reduction techniques based on random projection—0
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems—0
A Survey on Archetypal Analysis—0
A Survey on Design-space Dimensionality Reduction Methods for Shape Optimization—0
A Symmetric Rank-one Quasi Newton Method for Non-negative Matrix Factorization—0
Asymptotic Generalization Bound of Fisher's Linear Discriminant Analysis—0
A Systematic Study of Semantic Vector Space Model Parameters—0
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets—0
A Tangent Distance Preserving Dimensionality Reduction Algorithm—0
A Theoretical Analysis of Noisy Sparse Subspace Clustering on Dimensionality-Reduced Data—0
A theoretical contribution to the fast implementation of null linear discriminant analysis method using random matrix multiplication with scatter matrices—0
A topological classifier to characterize brain states: When shape matters more than variance—0
A Topological "Reading" Lesson: Classification of MNIST using TDA—0
A Trace Lasso Regularized L1-norm Graph Cut for Highly Correlated Noisy Hyperspectral Image—0
A t-SNE Based Classification Approach to Compositional Microbiome Data—0
Attention-based Supply-Demand Prediction for Autonomous Vehicles—0
Attention is all you need for Videos: Self-attention based Video Summarization using Universal Transformers—0
Attention or memory? Neurointerpretable agents in space and time—0
A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)—0
A Two-Stage Dual-Path Framework for Text Tampering Detection and Recognition—0
A Two-Stage Federated Learning Approach for Industrial Prognostics Using Large-Scale High-Dimensional Signals—0
Audio Visual Speech Recognition using Deep Recurrent Neural Networks—0
Augment on Manifold: Mixup Regularization with UMAP—0
A Unified Framework for Optimization-Based Graph Coarsening—0
A Unifying Family of Data-Adaptive Partitioning Algorithms—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