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 2376–2400 of 3304 papers

TitleStatusHype
Exact Cluster Recovery via Classical Multidimensional Scaling—0
Bi-Linear Modeling of Data Manifolds for Dynamic-MRI Recovery—0
Uncertainty Autoencoders: Learning Compressed Representations via Variational Information Maximization—0
Group Preserving Label Embedding for Multi-Label Classification—0
bigMap: Big Data Mapping with Parallelized t-SNE—0
A determinantal point process for column subset selection—0
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning—0
Random Projection in Deep Neural NetworksCode0
Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problemCode0
Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features—0
Deep Variational Sufficient Dimensionality Reduction—0
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach—0
A Low Effort Approach to Structured CNN Design Using PCA—0
Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach—0
A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)—0
Class Mean Vector Component and Discriminant Analysis—0
Graph Signal Representation with Wasserstein Barycenters—0
Classification of Cervical Cancer Dataset—0
Multi-Dimensional Scaling on Groups—0
Time Series Featurization via Topological Data Analysis—0
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder ApproachCode0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
SqueezeFit: Label-aware dimensionality reduction by semidefinite programmingCode0
Machine Learning of coarse-grained Molecular Dynamics Force Fields—0
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach—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