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 2101–2125 of 3304 papers

TitleStatusHype
Deep topic modeling by multilayer bootstrap network and lasso—0
Autoencoding with a Classifier SystemCode1
Lucene for Approximate Nearest-Neighbors Search on Arbitrary Dense Vectors—0
Unsupervised Boosting-based Autoencoder Ensembles for Outlier DetectionCode0
Supervised tensor decomposition with features on multiple modes—0
Multi-Band Multi-Resolution Fully Convolutional Neural Networks for Singing Voice SeparationCode0
Supporting Multi-point Fan Design with Dimension ReductionCode0
LEt-SNE: A Hybrid Approach To Data Embedding and Visualization of Hyperspectral ImageryCode0
Privacy-preserving Federated Bayesian Learning of a Generative Model for Imbalanced Classification of Clinical Data—0
A Topological "Reading" Lesson: Classification of MNIST using TDA—0
Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models—0
Earthmover-based manifold learning for analyzing molecular conformation spacesCode0
Multiclass spectral feature scaling method for dimensionality reduction—0
Roweis Discriminant Analysis: A Generalized Subspace Learning MethodCode0
Efficient Sketching Algorithm for Sparse Binary Data—0
A Multi-view Dimensionality Reduction Algorithm Based on Smooth Representation Model—0
ECA-Net: Efficient Channel Attention for Deep Convolutional Neural NetworksCode2
Hierarchical stochastic neighbor embedding as a tool for visualizing the encoding capability of magnetic resonance fingerprinting dictionaries—0
Elementos da teoria de aprendizagem de máquina supervisionada—0
Cascaded Gaussian Processes for Data-efficient Robot Dynamics Learning—0
Covariance-free Partial Least Squares: An Incremental Dimensionality Reduction MethodCode0
A Comparison Study on Nonlinear Dimension Reduction Methods with Kernel Variations: Visualization, Optimization and Classification—0
Fault Detection Using Nonlinear Low-Dimensional Representation of Sensor Data—0
Near-Convex Archetypal Analysis—0
Order-Preserving Wasserstein Discriminant Analysis—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