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 2776–2800 of 3304 papers

TitleStatusHype
Single-Pass PCA of Large High-Dimensional Data—0
Leveraging Patient Similarity and Time Series Data in Healthcare Predictive Models—0
Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning—0
Joint Semi-supervised RSS Dimensionality Reduction and Fingerprint Based Algorithm for Indoor Localization—0
MLC Toolbox: A MATLAB/OCTAVE Library for Multi-Label Classification—0
Enhancing Robustness of Machine Learning Systems via Data Transformations—0
Interactive Graphics for Visually Diagnosing Forest Classifiers in R—0
Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces—0
Contextual Bidirectional Long Short-Term Memory Recurrent Neural Network Language Models: A Generative Approach to Sentiment Analysis—0
The Informativeness of K -Means for Learning Mixture Models—0
Locality preserving projection on SPD matrix Lie group: algorithm and analysis—0
Microstructure under the Microscope: Tools to Survive and Thrive in The Age of (Too Much) Information—0
Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?—0
SIMLR: A Tool for Large-Scale Genomic Analyses by Multi-Kernel LearningCode0
PSF field learning based on Optimal Transport Distances—0
Neural method for Explicit Mapping of Quasi-curvature Locally Linear Embedding in image retrieval—0
Joint Embedding of GraphsCode0
Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy—0
Evaluating Graph Signal Processing for Neuroimaging Through Classification and Dimensionality Reduction—0
Applying Ricci Flow to High Dimensional Manifold Learning—0
The Unreasonable Effectiveness of Structured Random Orthogonal EmbeddingsCode1
L^3-SVMs: Landmarks-based Linear Local Support Vectors Machines—0
A description length approach to determining the number of k-means clusters—0
Semi-supervised Learning based on Distributionally Robust Optimization—0
Feasibility of Principal Component Analysis in hand gesture recognition system—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