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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 1901–1950 of 3304 papers

TitleStatusHype
Reduced Deep Convolutional Activation Features (R-DeCAF) in Histopathology Images to Improve the Classification Performance for Breast Cancer Diagnosis—0
Reduced-Rank Multi-objective Policy Learning and Optimization—0
Reducing Catastrophic Forgetting in Self Organizing Maps with Internally-Induced Generative Replay—0
Reducing Redundancy in the Bottleneck Representation of the Autoencoders—0
Reducing training requirements through evolutionary based dimension reduction and subject transfer—0
Regression-aware decompositions—0
Regression of high dimensional angular momentum states of light—0
Regularisation for PCA- and SVD-type matrix factorisations—0
Regularized Pooling—0
Regular Variation in Hilbert Spaces and Principal Component Analysis for Functional Extremes—0
Rehabilitating Isomap: Euclidean Representation of Geodesic Structure—0
Rehabilitation of Count-based Models for Word Vector Representations—0
Improving Surrogate Model Robustness to Perturbations for Dynamical Systems Through Machine Learning and Data Assimilation—0
Relation between PLS and OLS regression in terms of the eigenvalue distribution of the regressor covariance matrix—0
Parameter-wise co-clustering for high-dimensional data—0
Relevance-driven Decision Making for Safer and More Efficient Human Robot Collaboration—0
Relevance for Human Robot Collaboration—0
Reliable Distributed Clustering with Redundant Data Assignment—0
Representational Analysis of Binding in Language Models—0
Representation-Constrained Autoencoders and an Application to Wireless Positioning—0
Representation Learning by Reconstructing Neighborhoods—0
Representation learning of dynamic networks—0
Representation Learning on Graphs: Methods and Applications—0
Representation Loss Minimization with Randomized Selection Strategy for Efficient Environmental Fake Audio Detection—0
Representation of big data by dimension reduction—0
Representation Transfer for Differentially Private Drug Sensitivity Prediction—0
Representative period selection for power system planning using autoencoder-based dimensionality reduction—0
Representatividad Muestral en la Incertidumbre Simétrica Multivariada para la Selección de Atributos—0
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey—0
Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning—0
ResFeats: Residual Network Based Features for Image Classification—0
ReStoCNet: Residual Stochastic Binary Convolutional Spiking Neural Network for Memory-Efficient Neuromorphic Computing—0
Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey—0
Restricted Isometry Property of Gaussian Random Projection for Finite Set of Subspaces—0
Restricted Isometry Property under High Correlations—0
Rethinking Dimensionality Reduction in Grid-based 3D Object Detection—0
Rethinking the editing of generative adversarial networks: a method to estimate editing vectors based on dimension reduction—0
RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology—0
Revealing computational mechanisms of retinal prediction via model reduction—0
Reverse Markov Learning: Multi-Step Generative Models for Complex Distributions—0
Review of Single-cell RNA-seq Data Clustering for Cell Type Identification and Characterization—0
Review of Swarm Intelligence-based Feature Selection Methods—0
Revisiting Classical Multiclass Linear Discriminant Analysis with a Novel Prototype-based Interpretable Solution—0
Rewards-based image analysis in microscopy—0
Riemannian joint dimensionality reduction and dictionary learning on symmetric positive definite manifold—0
Riemannian Principal Component Analysis—0
Rigorous Restricted Isometry Property of Low-Dimensional Subspaces—0
RIPML: A Restricted Isometry Property based Approach to Multilabel Learning—0
Risk and optimal policies in bandit experiments—0
Risk factor aggregation and stress testing—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