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 1601–1625 of 3304 papers

TitleStatusHype
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
A Deep Signed Directional Distance Function for Object Shape Representation—0
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI—0
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty QuantificationCode0
Machine learning for assessing quality of service in the hospitality sector based on customer reviews—0
Measuring inter-cluster similarities with Alpha Shape TRIangulation in loCal Subspaces (ASTRICS) facilitates visualization and clustering of high-dimensional data—0
Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures—0
Identifying Layers Susceptible to Adversarial Attacks—0
WeightScale: Interpreting Weight Change in Neural Networks—0
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit—0
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—0
fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clustering—0
UCSL : A Machine Learning Expectation-Maximization framework for Unsupervised Clustering driven by Supervised LearningCode0
A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources—0
Randomized Dimensionality Reduction for Facility Location and Single-Linkage Clustering—0
Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons—0
Interactive Dimensionality Reduction for Comparative AnalysisCode0
Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey—0
Hyperspectral Remote Sensing Image Classification Based on Multi-scale Cross Graphic Convolution—0
Self-paced Principal Component Analysis—0
Feature Grouping and Sparse Principal Component Analysis with Truncated RegularizationCode0
Regularisation for PCA- and SVD-type matrix factorisations—0
Efficient Tensor Contraction via Fast Count Sketch—0
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study—0
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals—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