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Dimensionality Reduction

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

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Papers

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Showing 626–650 of 3304 papers

TitleStatusHype
Dynamic and Memory-efficient Shape Based Methodologies for User Type Identification in Smart Grid Applications—0
Nonlinear functional regression by functional deep neural network with kernel embedding—0
Scalable manifold learning by uniform landmark sampling and constrained locally linear embedding—0
Inference and Visualization of Community Structure in Attributed Hypergraphs Using Mixed-Membership Stochastic Block ModelsCode0
A Novel method for Schizophrenia classification using nonlinear features and neural networks—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language ModelsCode1
Agnostically Learning Multi-index Models with Queries—0
LeanVec: Searching vectors faster by making them fitCode2
Comparative Analysis of Radiomic Features and Gene Expression Profiles in Histopathology Data Using Graph Neural Networks—0
Nowcasting Madagascar's real GDP using machine learning algorithms—0
Efficient Estimation of the Central Mean Subspace via Smoothed Gradient Outer Products—0
Deep Learning for Efficient GWAS Feature Selection—0
Augment on Manifold: Mixup Regularization with UMAP—0
Convergence Visualizer of Decentralized Federated Distillation with Reduced Communication Costs—0
Characterization of Phosphorylated Tau-Microtubule complex with Molecular Dynamics (MD) simulationCode0
Unsupervised Learning for Fault Detection of HVAC Systems: An OPTICS -based Approach for Terminal Air Handling Units—0
Language-Assisted 3D Scene Understanding—0
A Powerful Face Preprocessing For Robust Kinship Verification based Tensor Analyses—0
Physics-Informed Representation and Learning: Control and Risk QuantificationCode0
Investigating Shallow and Deep Learning Techniques for Emotion Classification in Short Persian TextsCode0
Exploring UMAP in hybrid models of entropy-based and representativeness sampling for active learning in biomedical segmentation—0
A new method color MS-BSIF Features learning for the robust kinship verification—0
Symplectic Autoencoders for Model Reduction of Hamiltonian SystemsCode1
Data-Driven Socio-Economic Deprivation Prediction via Dimensionality Reduction: The Power of Diffusion MapsCode0
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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