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 2601–2610 of 3304 papers

TitleStatusHype
A kernel Principal Component Analysis (kPCA) digest with a new backward mapping (pre-image reconstruction) strategy—0
A Knowledge-based Representation for Cross-Language Document Retrieval and Categorization—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
A Latent Variable Model for Two-Dimensional Canonical Correlation Analysis and its Variational Inference—0
Algorithm-Agnostic Interpretations for Clustering—0
Algorithmic Stability and Uniform Generalization—0
Algorithms for Approximate Subtropical Matrix Factorization—0
A Light weight and Hybrid Deep Learning Model based Online Signature Verification—0
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification—0
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks—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