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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 2551–2575 of 3304 papers

TitleStatusHype
A description length approach to determining the number of k-means clusters—0
A determinantal point process for column subset selection—0
A Dimensionality Reduction Approach for Convolutional Neural Networks—0
A Dimensionality Reduction Method for Finding Least Favorable Priors with a Focus on Bregman Divergence—0
A dimensionality reduction technique based on the Gromov-Wasserstein distance—0
A Discussion On the Validity of Manifold Learning—0
A DNA Methylation Classification Model Predicts Organ and Disease Site—0
ADRS-CNet: An adaptive dimensionality reduction selection and classification network for DNA storage clustering algorithms—0
Advanced User Credit Risk Prediction Model using LightGBM, XGBoost and Tabnet with SMOTEENN—0
Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity—0
Adversarial Vulnerability as a Consequence of On-Manifold Inseparibility—0
A dynamical systems based framework for dimension reduction—0
AEkNN: An AutoEncoder kNN-based classifier with built-in dimensionality reduction—0
A Faster Approach to Spiking Deep Convolutional Neural Networks—0
A Second-Order Majorant Algorithm for Nonnegative Matrix Factorization—0
A fast, universal algorithm to learn parametric nonlinear embeddings—0
A feature construction framework based on outlier detection and discriminative pattern mining—0
A Flexible Iterative Framework for Consensus Clustering—0
A framework for streamlined statistical prediction using topic models—0
A Fully Convolutional Network for MR Fingerprinting—0
A Functional approach for Two Way Dimension Reduction in Time Series—0
A Fuzzy Approach for Feature Evaluation and Dimensionality Reduction to Improve the Quality of Web Usage Mining Results—0
A generalized flow for multi-class and binary classification tasks: An Azure ML approach—0
A Generalized Mean Approach for Distributed-PCA—0
A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data—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