SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 101–125 of 3304 papers

TitleStatusHype
A New Basis for Sparse Principal Component AnalysisCode1
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decompositionCode1
EVNet: An Explainable Deep Network for Dimension ReductionCode1
Explaining dimensionality reduction results using Shapley valuesCode1
Fast and Accurate Network Embeddings via Very Sparse Random ProjectionCode1
Fast conformational clustering of extensive molecular dynamics simulation dataCode1
An Embedding is Worth a Thousand Noisy LabelsCode1
Few-Shot Learning by Dimensionality Reduction in Gradient SpaceCode1
FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language ModelsCode1
Flows for simultaneous manifold learning and density estimationCode1
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
On Path Integration of Grid Cells: Group Representation and Isotropic ScalingCode1
Going Beyond T-SNE: Exposing whatlies in Text EmbeddingsCode1
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size DataCode1
A Memory Efficient Baseline for Open Domain Question AnsweringCode1
High-dimensional additive Gaussian processes under monotonicity constraintsCode1
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical ProjectionsCode1
HUMAP: Hierarchical Uniform Manifold Approximation and ProjectionCode1
Invertible Concept-based Explanations for CNN Models with Non-negative Concept Activation VectorsCode1
Improving Metric Dimensionality Reduction with Distributed TopologyCode1
Interpreting Temporal Graph Neural Networks with Koopman TheoryCode1
A local approach to parameter space reduction for regression and classification tasksCode1
An Additive Autoencoder for Dimension EstimationCode1
Latent Autoregressive Source SeparationCode1
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box ModelsCode1
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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