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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 2376–2400 of 3304 papers

TitleStatusHype
Using Deep Autoencoders for Facial Expression Recognition—0
Using Dimension Reduction to Improve the Classification of High-dimensional Data—0
Using Lexical Expansion to Learn Inference Rules from Sparse Data—0
Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems—0
Using Non-invertible Data Transformations to Build Adversarial-Robust Neural Networks—0
Using PCA and Factor Analysis for Dimensionality Reduction of Bio-informatics Data—0
Using PCA to Efficiently Represent State Spaces—0
Using pseudo-senses for improving the extraction of synonyms from word embeddings—0
Using the left Gram matrix to cluster high dimensional data—0
Using Topological Data Analysis to classify Encrypted Bits—0
UTD-CRSS Systems for 2016 NIST Speaker Recognition Evaluation—0
Utilizing Machine Learning and 3D Neuroimaging to Predict Hearing Loss: A Comparative Analysis of Dimensionality Reduction and Regression Techniques—0
Utilizing VQ-VAE for End-to-End Health Indicator Generation in Predicting Rolling Bearing RUL—0
VAE-KRnet and its applications to variational Bayes—0
Shuttle Between the Instructions and the Parameters of Large Language Models—0
Variable noise and dimensionality reduction for sparse Gaussian processes—0
Variational Autoencoders for Feature Detection of Magnetic Resonance Imaging Data—0
Variational autoencoders for tissue heterogeneity exploration from (almost) no preprocessed mass spectrometry imaging data—0
Variational Bayesian Optimal Experimental Design with Normalizing Flows—0
Variational Bayesian surrogate modelling with application to robust design optimisation—0
Variational embedding of protein folding simulations using gaussian mixture variational autoencoders—0
Variational Gaussian Process Dynamical Systems—0
Variational Inference for Uncertainty on the Inputs of Gaussian Process Models—0
Variational Koopman models: slow collective variables and molecular kinetics from short off-equilibrium simulations—0
Variational Learning of Gaussian Process Latent Variable Models through Stochastic Gradient Annealed Importance Sampling—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