SOTAVerified

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 1801–1825 of 3304 papers

TitleStatusHype
Algorithmic Stability and Generalization of an Unsupervised Feature Selection AlgorithmCode1
Auto-Encoding Variational Bayes for Inferring Topics and VisualizationCode0
Prediction of daily maximum ozone levels using Lasso sparse modeling method—0
Understanding Information Processing in Human Brain by Interpreting Machine Learning ModelsCode1
Interactive Latent Interpolation on MNIST DatasetCode0
Mycorrhiza: Genotype Assignment usingPhylogenetic NetworksCode0
Rapid Robust Principal Component Analysis: CUR Accelerated Inexact Low Rank EstimationCode0
Causal learning with sufficient statistics: an information bottleneck approach—0
0-dimensional Homology Preserving Dimensionality Reduction with TopoMap—0
Circular Coordinate Methods with Generalized Penalty Functions—0
Challenging Euclidean Topological AutoencodersCode0
Causal Feature Selection with Dimension Reduction for Interpretable Text Classification—0
Explanation and Use of Uncertainty Quantified by Bayesian Neural Network Classifiers for Breast Histopathology Images—0
Invertible Manifold Learning for Dimension ReductionCode0
Less is more: Faster and better music version identification with embedding distillationCode1
Combination of digital signal processing and assembled predictive models facilitates the rational design of proteins—0
Optimal High-order Tensor SVD via Tensor-Train Orthogonal IterationCode0
Parameter Optimization using high-dimensional Bayesian Optimization—0
Factorized Discriminant Analysis for Genetic Signatures of Neuronal PhenotypesCode0
Algorithms for Nonnegative Matrix Factorization with the Kullback-Leibler DivergenceCode0
Bayesian Feature Selection in Joint Quantile Time Series Analysis—0
Perplexity-free Parametric t-SNECode1
Effective Sample Size, Dimensionality, and Generalization in Covariate Shift AdaptationCode1
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications—0
Modifying the Symbolic Aggregate Approximation Method to Capture Segment Trend Information—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