SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 301–310 of 3304 papers

TitleStatusHype
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering—0
Unsupervised feature selection algorithm framework based on neighborhood interval disturbance fusion—0
Lossless KV Cache Compression to 2%—0
Efficient Sparse PCA via Block-Diagonalization—0
Interpreting Temporal Graph Neural Networks with Koopman TheoryCode1
Large data limits and scaling laws for tSNE—0
Approaching Metaheuristic Deep Learning Combos for Automated Data Mining—0
Game Theory Meets Statistical Mechanics in Deep Learning Design—0
EEG-based 90-Degree Turn Intention Detection for Brain-Computer Interface—0
GleanVec: Accelerating vector search with minimalist nonlinear dimensionality reduction—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