SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 901–950 of 3304 papers

TitleStatusHype
Auto-encoding brain networks with applications to analyzing large-scale brain imaging datasets—0
Dimensionality Reduction for Categorical Data—0
Dimensionality reduction for click-through rate prediction: Dense versus sparse representation—0
Dimensionality Reduction for Data in Multiple Feature Representations—0
Dimensionality Reduction for General KDE Mode Finding—0
A Multi-Fidelity Methodology for Reduced Order Models with High-Dimensional Inputs—0
Dimensionality Reduction for k-Means Clustering and Low Rank Approximation—0
Dimensionality Reduction for k-means Clustering—0
Classification of high-dimensional data with spiked covariance matrix structure—0
Dimensionality reduction for prediction: Application to Bitcoin and Ethereum—0
Dimensionality Reduction for Representing the Knowledge of Probabilistic Models—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—0
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization—0
Dimensionality reduction for time series data—0
Dimensionality Reduction for Tukey Regression—0
Dimensionality Reduction for Wasserstein Barycenter—0
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds—0
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer—0
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification—0
Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform—0
Automatic dimensionality reduction of Twin-in-the-Loop Observers—0
Dimensionality reduction methods for molecular simulations—0
Dimensionality Reduction of Affine Variational Inequalities Using Random Projections—0
Dimensionality Reduction of Collective Motion by Principal Manifolds—0
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning—0
Classification of EEG Signals using Genetic Programming for Feature Construction—0
An Improved CNN-based Neural Network Model for Fruit Sugar Level Detection—0
Dimensionality Reduction of Movement Primitives in Parameter Space—0
A framework for streamlined statistical prediction using topic models—0
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective—0
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods—0
Automatic Selection of t-SNE Perplexity—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Dimensionality reduction techniques to support insider trading detection—0
Dimensionality reduction: theoretical perspective on practical measures—0
Classification of Cervical Cancer Dataset—0
Dimensionality Reduction using Elastic Measures—0
Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster Analysis—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
Dimensionality Reduction Using the Sparse Linear Model—0
Dimensionality Reduction via Diffusion Map Improved with Supervised Linear Projection—0
Dimensionality reduction via path integration for computing mRNA distributions—0
Dimensionality Reduction via Regression in Hyperspectral Imagery—0
Dimensionality reduction with missing values imputation—0
Dimensionality reduction with subgaussian matrices: a unified theory—0
Dimensionality Reduction with Subspace Structure Preservation—0
Classification Methods Based on Machine Learning for the Analysis of Fetal Health Data—0
Dimension Estimation Using Random Connection Models—0
AVIDA: Alternating method for Visualizing and Integrating Data—0
An Improved Deep Learning Model for Word Embeddings Based Clustering for Large Text Datasets—0
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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