SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1621–1630 of 3304 papers

TitleStatusHype
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
Low Dimensional Landscape Hypothesis is True: DNNs can be Trained in Tiny SubspacesCode1
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges—0
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack—0
On the Whitney near extension problem, BMO, alignment of data, best approximation in algebraic geometry, manifold learning and their beautiful connections: A modern treatmentCode0
Meta-learning of Pooling Layers for Character RecognitionCode0
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration MethodCode1
Revisiting Dynamic Convolution via Matrix DecompositionCode1
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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