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 101–125 of 3304 papers

TitleStatusHype
Approximating Likelihood Ratios with Calibrated Discriminative ClassifiersCode1
Flows for simultaneous manifold learning and density estimationCode1
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reductionCode1
Generative Locally Linear EmbeddingCode1
On Path Integration of Grid Cells: Group Representation and Isotropic ScalingCode1
Effective Sample Size, Dimensionality, and Generalization in Covariate Shift AdaptationCode1
A Spectral Method for Assessing and Combining Multiple Data VisualizationsCode1
Graph Convolutional Network-based Feature Selection for High-dimensional and Low-sample Size DataCode1
DataLens: Scalable Privacy Preserving Training via Gradient Compression and AggregationCode1
HEFT: Homomorphically Encrypted Fusion of Biometric TemplatesCode1
AUCO ResNet: an end-to-end network for Covid-19 pre-screening from cough and breathCode1
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
Autoencoding with a Classifier SystemCode1
Binary Bleed: Fast Distributed and Parallel Method for Automatic Model SelectionCode1
Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor ClassificationCode1
BasisVAE: Translation-invariant feature-level clustering with Variational AutoencodersCode1
Balanced Neural ODEs: nonlinear model order reduction and Koopman operator approximationsCode1
Clustering with UMAP: Why and How Connectivity MattersCode1
Bayesian Optimization of Sampling Densities in MRICode1
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
CBMAP: Clustering-based manifold approximation and projection for dimensionality reductionCode1
Joint and Progressive Subspace Analysis (JPSA) with Spatial-Spectral Manifold Alignment for Semi-Supervised Hyperspectral Dimensionality ReductionCode1
Collection Space Navigator: An Interactive Visualization Interface for Multidimensional DatasetsCode1
Latent Autoregressive Source SeparationCode1
CatBoost: gradient boosting with categorical features supportCode1
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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