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 2501–2525 of 3304 papers

TitleStatusHype
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation—0
A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering—0
A clustering adaptive Gaussian process regression method: response patterns based real-time prediction for nonlinear solid mechanics problems—0
A cognitive based Intrusion detection system—0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
A Compact and Discriminative Face Track Descriptor—0
A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques—0
A Comparative Study of Text Preprocessing Approaches for Topic Detection of User Utterances—0
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
A Comparison of Neural Network Training Methods for Text Classification—0
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD—0
A Comparison Study on Nonlinear Dimension Reduction Methods with Kernel Variations: Visualization, Optimization and Classification—0
A Comprehensive Filter Feature Selection for Improving Document Classification—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics—0
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources—0
A convex formulation for high-dimensional sparse sliced inverse regression—0
A Convex formulation for linear discriminant analysis—0
A Convex Formulation for Spectral Shrunk Clustering—0
A Convex Sparse PCA for Feature Analysis—0
A Correspondence Analysis Framework for Author-Conference Recommendations—0
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA—0
A Critical Note on the Evaluation of Clustering Algorithms—0
A Cross Entropy test allows quantitative statistical comparison of t-SNE and UMAP representations—0
Show:102550
← PrevPage 101 of 133Next →
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