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 2191–2200 of 3304 papers

TitleStatusHype
LayerFlow: Layer-wise Exploration of LLM Embeddings using Uncertainty-aware Interlinked Projections—0
Learnable Faster Kernel-PCA for Nonlinear Fault Detection: Deep Autoencoder-Based Realization—0
Learnable Mixed-precision and Dimension Reduction Co-design for Low-storage Activation—0
Learned SVD: solving inverse problems via hybrid autoencoding—0
Learning a Deep Part-based Representation by Preserving Data Distribution—0
Learning a Discriminative Null Space for Person Re-identification—0
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas—0
Learning-Augmented Sketches for Hessians—0
Learning automata based SVM for intrusion detection—0
Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations—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