SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Showing 151200 of 3304 papers

TitleStatusHype
DeepView: Visualizing Classification Boundaries of Deep Neural Networks as Scatter Plots Using Discriminative Dimensionality ReductionCode1
DefakeHop: A Light-Weight High-Performance Deepfake DetectorCode1
Detection and Retrieval of Out-of-Distribution Objects in Semantic SegmentationCode1
Diagnosing and Fixing Manifold Overfitting in Deep Generative ModelsCode1
A local approach to parameter space reduction for regression and classification tasksCode1
Dimension Reduction for Efficient Dense Retrieval via Conditional AutoencoderCode1
DiRe-JAX: A JAX based Dimensionality Reduction Algorithm for Large-scale DataCode1
On Path Integration of Grid Cells: Group Representation and Isotropic ScalingCode1
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breathCode1
DMT-HI: MOE-based Hyperbolic Interpretable Deep Manifold Transformation for Unspervised Dimensionality ReductionCode1
Drop-Bottleneck: Learning Discrete Compressed Representation for Noise-Robust ExplorationCode1
Adversarial AutoencodersCode1
Enhanced MRI brain tumor detection and classification via topological data analysis and low-rank tensor decompositionCode1
Equivariant Wavelets: Fast Rotation and Translation Invariant Wavelet Scattering TransformsCode1
Towards a More Rigorous Science of Blindspot Discovery in Image Classification ModelsCode1
EVNet: An Explainable Deep Network for Dimension ReductionCode1
Application of Clustering Algorithms for Dimensionality Reduction in Infrastructure Resilience Prediction ModelsCode1
Fast Network Embedding Enhancement via High Order Proximity ApproximationCode1
A new computationally efficient algorithm to solve Feature Selection for Functional Data Classification in high-dimensional spacesCode1
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box ModelsCode1
FILP-3D: Enhancing 3D Few-shot Class-incremental Learning with Pre-trained Vision-Language ModelsCode1
Flows for simultaneous manifold learning and density estimationCode1
Approximating Likelihood Ratios with Calibrated Discriminative ClassifiersCode1
GCN-DevLSTM: Path Development for Skeleton-Based Action RecognitionCode1
Going Beyond T-SNE: Exposing whatlies in Text EmbeddingsCode1
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size DataCode1
HEFT: Homomorphically Encrypted Fusion of Biometric TemplatesCode1
A Hybrid Architecture for Out of Domain Intent Detection and Intent DiscoveryCode1
An Embedding is Worth a Thousand Noisy LabelsCode1
An efficient aggregation method for the symbolic representation of temporal dataCode1
ActUp: Analyzing and Consolidating tSNE and UMAPCode1
Hybrid Quantum-Classical Generative Adversarial Network for High Resolution Image GenerationCode1
Improving Metric Dimensionality Reduction with Distributed TopologyCode1
Improving the HardNet DescriptorCode1
A New Basis for Sparse Principal Component AnalysisCode1
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality ReductionCode1
An Additive Autoencoder for Dimension EstimationCode1
Latent Autoregressive Source SeparationCode1
A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithmsCode1
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
Less is more: Faster and better music version identification with embedding distillationCode1
Level set learning with pseudo-reversible neural networks for nonlinear dimension reduction in function approximationCode1
Linear Recursive Feature Machines provably recover low-rank matricesCode1
Locally Linear Embedding and its Variants: Tutorial and SurveyCode1
Markov-Lipschitz Deep LearningCode1
Metric Space Magnitude for Evaluating the Diversity of Latent RepresentationsCode1
Minimum-Distortion EmbeddingCode1
Modern Dimension ReductionCode1
The Signature Kernel is the solution of a Goursat PDECode1
Distributional Principal AutoencodersCode1
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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