SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 126–150 of 3304 papers

TitleStatusHype
A Spectral Method for Assessing and Combining Multiple Data VisualizationsCode1
Adversarial AutoencodersCode1
An Additive Autoencoder for Dimension EstimationCode1
An efficient aggregation method for the symbolic representation of temporal dataCode1
A local approach to parameter space reduction for regression and classification tasksCode1
A New Basis for Sparse Principal Component AnalysisCode1
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithmsCode1
A Primer on the Signature Method in Machine LearningCode1
A tutorial on generalized eigendecomposition for denoising, contrast enhancement, and dimension reduction in multichannel electrophysiologyCode1
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breathCode1
Algorithmic Stability and Generalization of an Unsupervised Feature Selection AlgorithmCode1
Autoencoding with a Classifier SystemCode1
BasisVAE: Translation-invariant feature-level clustering with Variational AutoencodersCode1
Bayesian Optimization of Sampling Densities in MRICode1
A hyperparameter-tuning approach to automated inverse planningCode1
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
Clustering with UMAP: Why and How Connectivity MattersCode1
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional DatasetsCode1
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reductionCode1
Correlation-based feature selection to identify functional dynamics in proteinsCode1
DartMinHash: Fast Sketching for Weighted SetsCode1
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brainCode1
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box ModelsCode1
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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