SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 2101–2150 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
MASS-UMAP: Fast and accurate analog ensemble search in weather radar archive—0
TriMap: Large-scale Dimensionality Reduction Using TripletsCode0
Specializing Word Embeddings (for Parsing) by Information BottleneckCode0
A Quotient Space Formulation for Generative Statistical Analysis of Graphical DataCode0
Manifold Fitting in Ambient Space—0
Capacity Preserving Mapping for High-dimensional Data VisualizationCode0
Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings—0
Siamese Neural Networks for Wireless Positioning and Channel Charting—0
A New Covariance Estimator for Sufficient Dimension Reduction in High-Dimensional and Undersized Sample Problems—0
Netboost: Boosting-supported network analysis improves high-dimensional omics prediction in acute myeloid leukemia and Huntington's disease—0
Realtime Simulation of Thin-Shell Deformable Materials using CNN-Based Mesh Embedding—0
Estimating covariance and precision matrices along subspaces—0
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario—0
Function Preserving Projection for Scalable Exploration of High-Dimensional DataCode0
The space complexity of inner product filters—0
Dimension Estimation Using Autoencoders—0
Manifold Fitting under Unbounded NoiseCode0
Application of Fuzzy Clustering for Text Data Dimensionality Reduction—0
Uncertainty Quantification in Stochastic Economic Dispatch using Gaussian Process Emulation—0
Interpreting Distortions in Dimensionality Reduction by Superimposing Neighbourhood Graphs—0
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality ReductionCode1
Interpretable Discriminative Dimensionality Reduction and Feature Selection on the Manifold—0
Data Mapping and Finite Difference LearningCode0
Laplacian Matrix for Dimensionality Reduction and Clustering—0
PixelHop: A Successive Subspace Learning (SSL) Method for Object ClassificationCode0
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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