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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 1851–1875 of 3304 papers

TitleStatusHype
Quantized Random Projections and Non-Linear Estimation of Cosine Similarity—0
Quantum artificial vision for defect detection in manufacturing—0
Quantum classification of the MNIST dataset with Slow Feature Analysis—0
Quantum Cognition Machine Learning for Forecasting Chromosomal Instability—0
Quantum diffusion map for nonlinear dimensionality reduction—0
Self-Adaptive Quantum Kernel Principal Components Analysis for Compact Readout of Chemiresistive Sensor Arrays—0
Quantum Laplacian Eigenmap—0
Quantum Machine Learning Applied to the Classification of Diabetes—0
Quick unsupervised hyperspectral dimensionality reduction for earth observation: a comparison—0
Quotient Based Multiresolution Image Fusion of Thermal and Visual Images Using Daubechies Wavelet Transform for Human Face Recognition—0
RAINER: A Robust Ensemble Learning Grid Search-Tuned Framework for Rainfall Patterns Prediction—0
Random Forest Autoencoders for Guided Representation Learning—0
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering—0
Randomized Dimensionality Reduction for Euclidean Maximization and Diversity Measures—0
Randomized Dimensionality Reduction for k-means Clustering—0
Randomized Dimension Reduction on Massive Data—0
Randomized Dimension Reduction with Statistical Guarantees—0
Randomized ICA and LDA Dimensionality Reduction Methods for Hyperspectral Image Classification—0
Randomized Iterative Algorithms for Fisher Discriminant Analysis—0
A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression—0
Randomized Principal Component Analysis for Hyperspectral Image Classification—0
Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold—0
Randomized Sketches of Convex Programs with Sharp Guarantees—0
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction—0
Random Manhattan Integer Indexing: Incremental L1 Normed Vector Space Construction—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