SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 376–400 of 3304 papers

TitleStatusHype
A Radiomics-Incorporated Deep Ensemble Learning Model for Multi-Parametric MRI-based Glioma Segmentation—0
A Reconfigurable Low Power High Throughput Architecture for Deep Network Training—0
A Recurrent Probabilistic Neural Network with Dimensionality Reduction Based on Time-series Discriminant Component Analysis—0
A Knowledge-based Representation for Cross-Language Document Retrieval and Categorization—0
Are Latent Factor Regression and Sparse Regression Adequate?—0
Approximation Algorithms for Sparse Principal Component Analysis—0
A Hybrid Deep Learning CNN Model for Enhanced COVID-19 Detection from Computed Tomography (CT) Scan Images—0
A Review, Framework and R toolkit for Exploring, Evaluating, and Comparing Visualizations—0
A review of unsupervised learning in astronomy—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
A Robust and Efficient Boundary Point Detection Method by Measuring Local Direction Dispersion—0
A Robust Approach for Securing Audio Classification Against Adversarial Attacks—0
Artificial Intelligence and Dimensionality Reduction: Tools for approaching future communications—0
A selective review of sufficient dimension reduction for multivariate response regression—0
A Semiparametric Approach to Interpretable Machine Learning—0
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
A Simple and Effective Approach to Robust Unsupervised Bilingual Dictionary Induction—0
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding—0
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification—0
Approximate Matrix Multiplication with Application to Linear Embeddings—0
Split Semantic Detection in Sandplay Images—0
Accelerating hyperbolic t-SNE—0
Approximate Grassmannian Intersections: Subspace-Valued Subspace Learning—0
A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre—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