SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1711–1720 of 3304 papers

TitleStatusHype
Meta-learning of Pooling Layers for Character RecognitionCode0
On the Whitney near extension problem, BMO, alignment of data, best approximation in algebraic geometry, manifold learning and their beautiful connections: A modern treatmentCode0
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation—0
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks—0
Grassmann Graph Embedding—0
Empirical comparison between autoencoders and traditional dimensionality reduction methods—0
Simplicial RegularizationCode0
Low-Rank Isomap Algorithm—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