SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 626650 of 3304 papers

TitleStatusHype
Feature Learning for Fault Detection in High-Dimensional Condition-Monitoring SignalsCode0
An evaluation framework for dimensionality reduction through sectional curvatureCode0
Featurizing Koopman Mode Decomposition For Robust ForecastingCode0
Differentiable VQ-VAE's for Robust White Matter Streamline EncodingsCode0
Derivative-enhanced Deep Operator NetworkCode0
Fisher and Kernel Fisher Discriminant Analysis: TutorialCode0
Fisher Discriminant Triplet and Contrastive Losses for Training Siamese NetworksCode0
Designing Illuminant Spectral Power Distributions for Surface ClassificationCode0
Degradation Modeling and Prognostic Analysis Under Unknown Failure ModesCode0
From Gameplay to Symbolic Reasoning: Learning SAT Solver Heuristics in the Style of Alpha(Go) ZeroCode0
From Images to Features: Unbiased Morphology Classification via Variational Auto-Encoders and Domain AdaptationCode0
Linear and Quadratic Discriminant Analysis: TutorialCode0
Fully Convolutional Variational Autoencoder For Feature Extraction Of Fire Detection SystemCode0
Demixed principal component analysis of population activity in higher cortical areas reveals independent representation of task parametersCode0
Detecting Adversarial Examples through Nonlinear Dimensionality ReductionCode0
DiffRed: Dimensionality Reduction guided by stable rankCode0
Calibrating dimension reduction hyperparameters in the presence of noiseCode0
Caffe: Convolutional Architecture for Fast Feature EmbeddingCode0
Genomic data analysis in tree spacesCode0
Deep Temporal Clustering: Fully unsupervised learning of time-domain featuresCode0
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt PerspectiveCode0
Deep Random Splines for Point Process Intensity Estimation of Neural Population DataCode0
Graph Convolutional Networks Meet with High Dimensionality ReductionCode0
GraphTSNE: A Visualization Technique for Graph-Structured DataCode0
Deep Temporal Clustering : Fully Unsupervised Learning of Time-Domain FeaturesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy90.9Unverified
2tSNEClassification Accuracy51.5Unverified
3IVISClassification Accuracy46.6Unverified
4UMAPClassification Accuracy41.3Unverified
#ModelMetricClaimedVerifiedStatus
1UDRNClassification Accuracy71.1Unverified
2QSClassification Accuracy68Unverified