SOTAVerified

Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2901–2925 of 3304 papers

TitleStatusHype
Random projections of random manifolds—0
Fitting a Simplicial Complex using a Variation of k-means—0
Incomplete Pivoted QR-based Dimensionality Reduction—0
Convergence rates of Kernel Conjugate Gradient for random design regression—0
Improving Sparse Representation-Based Classification Using Local Principal Component Analysis—0
Text comparison using word vector representations and dimensionality reduction—0
Out-of-Sample Extension for Dimensionality Reduction of Noisy Time Series—0
Manifold Approximation by Moving Least-Squares Projection (MMLS)—0
Continuum directions for supervised dimension reduction—0
An Efficient Large-scale Semi-supervised Multi-label Classifier Capable of Handling Missing labels—0
A New Approach to Dimensionality Reduction for Anomaly Detection in Data Traffic—0
Bayesian Inference on Matrix Manifolds for Linear Dimensionality Reduction—0
Unsupervised Non Linear Dimensionality Reduction Machine Learning methods applied to Multiparametric MRI in cerebral ischemia: Preliminary Results—0
Discovery of Latent Factors in High-dimensional Data Using Tensor Methods—0
Sifting Common Information from Many VariablesCode0
A Tale of Two Bases: Local-Nonlocal Regularization on Image Patches with Convolution Framelets—0
Property-driven State-Space Coarsening for Continuous Time Markov Chains—0
Sequential Principal Curves Analysis—0
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis—0
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations—0
Efficient Large-Scale Similarity Search Using Matrix Factorization—0
Decoding Emotional Experience through Physiological Signal Processing—0
Mixtape Application: Music Map Methodology and Evaluation—0
Learning From Hidden Traits: Joint Factor Analysis and Latent Clustering—0
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods—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