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 2351–2400 of 3304 papers

TitleStatusHype
Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold—0
ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing—0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"—0
Machine Learning With Feature Selection Using Principal Component Analysis for Malware Detection: A Case Study—0
Distance metric learning based on structural neighborhoods for dimensionality reduction and classification performance improvement—0
Can Genetic Programming Do Manifold Learning Too?—0
License Plate Recognition with Compressive Sensing Based Feature Extraction—0
Principal Model Analysis Based on Partial Least Squares—0
Riemannian optimization with a preconditioning scheme on the generalized Stiefel manifold—0
Minimum description length as an objective function for non-negative matrix factorization—0
A Tangent Distance Preserving Dimensionality Reduction Algorithm—0
Higher-order Count Sketch: Dimensionality Reduction That Retains Efficient Tensor Operations—0
Distinguishing between Normal and Cancer Cells Using Autoencoder Node Saliency—0
Distributionally Robust and Multi-Objective Nonnegative Matrix Factorization—0
Representation Transfer for Differentially Private Drug Sensitivity Prediction—0
Throttling Malware Families in 2DCode0
A Deep Learning Framework for Assessing Physical Rehabilitation ExercisesCode0
Diseño de un espacio semántico sobre la base de la Wikipedia. Una propuesta de análisis de la semántica latente para el idioma español—0
Stochastic Linear Bandits with Hidden Low Rank Structure—0
On the cross-validation bias due to unsupervised pre-processingCode0
Comparing of Term Clustering Frameworks for Modular Ontology Learning—0
Empowering individual trait prediction using interactions—0
Computer Vision and Metrics Learning for Hypothesis Testing: An Application of Q-Q Plot for Normality Test—0
Coupling the reduced-order model and the generative model for an importance sampling estimator—0
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problemsCode0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
Image retrieval method based on CNN and dimension reduction—0
A witness function based construction of discriminative models using Hermite polynomials—0
Transfer Representation Learning with TSK Fuzzy System—0
FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signals—0
Randomized Tensor Ring Decomposition and Its Application to Large-scale Data Reconstruction—0
Performance prediction of data streams on high-performance architecture—0
Stochastic Approximation Algorithms for Principal Component Analysis—0
Auto-weighted Mutli-view Sparse Reconstructive Embedding—0
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections—0
Active Learning with TensorBoard Projector—0
Trigonometric comparison measure: A feature selection method for text categorization—0
Supervised Multiscale Dimension Reduction for Spatial Interaction Networks—0
Exact Cluster Recovery via Classical Multidimensional Scaling—0
Determining Principal Component Cardinality through the Principle of Minimum Description Length—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
Random Projection in Deep Neural NetworksCode0
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning—0
Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features—0
Nonlinear demixed component analysis for neural population data as a low-rank kernel regression problemCode0
Deep Variational Sufficient Dimensionality Reduction—0
Show:102550
← PrevPage 48 of 67Next →
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