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 2276–2300 of 3304 papers

TitleStatusHype
Localizing Persona Representations in LLMs—0
Locally Differentially Private Naive Bayes Classification—0
Locally Linear Embedding and fMRI feature selection in psychiatric classification—0
Locally Linear Unsupervised Feature Selection—0
Local Neighbor Propagation Embedding—0
Local Shrunk Discriminant Analysis (LSDA)—0
Loc-VAE: Learning Structurally Localized Representation from 3D Brain MR Images for Content-Based Image Retrieval—0
Logical Interpretations of Autoencoders—0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based ROMs—0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based reduced order models—0
Lossless KV Cache Compression to 2%—0
Low-complexity 8-point DCT Approximation Based on Angle Similarity for Image and Video Coding—0
Low-complexity Point Cloud Filtering for LiDAR by PCA-based Dimension Reduction—0
Low-complexity Rounded KLT Approximation for Image Compression—0
Low-dimensional approximations of the conditional law of Volterra processes: a non-positive curvature approach—0
Low-dimensional Data-based Surrogate Model of a Continuum-mechanical Musculoskeletal System Based on Non-intrusive Model Order Reduction—0
Low Dimensional Dynamics of Globally Coupled Complex Riccati Equations: Exact Firing-rate Equations for Spiking Neurons with Clustered Substructure—0
Low-Dimensional Gradient Helps Out-of-Distribution Detection—0
Low-Dimensional Representation-Driven TSK Fuzzy System for Feature Selection—0
Near-Linear Time and Fixed-Parameter Tractable Algorithms for Tensor Decompositions—0
Low-Rank Boolean Matrix Approximation by Integer Programming—0
Low-Rank Isomap Algorithm—0
Low-Rank Nonlinear Decoding of μ-ECoG from the Primary Auditory Cortex—0
Low-Rank Robust Subspace Tensor Clustering for Metro Passenger Flow Modeling—0
LSDAT: Low-Rank and Sparse Decomposition for Decision-based Adversarial Attack—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