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Dimensionality Reduction

Dimensionality reduction is the task of reducing the dimensionality of a dataset.

( Image credit: openTSNE )

Papers

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Showing 2676–2700 of 3304 papers

TitleStatusHype
Probabilistic partition of unity networks for high-dimensional regression problems—0
Probabilistic Principal Geodesic Analysis—0
Probabilistic Relational PCA—0
Probability distributions for analog-to-target distances—0
Probing Criticality in Quantum Spin Chains with Neural Networks—0
Probing Latent Subspaces in LLM for AI Security: Identifying and Manipulating Adversarial States—0
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation—0
Progressive Disentanglement Using Relevant Factor VAE—0
Weight Matrix Dimensionality Reduction in Deep Learning via Kronecker Multi-layer ArchitecturesCode0
Conditional Latent Block Model: a Multivariate Time Series Clustering Approach for Autonomous Driving ValidationCode0
Dimensionality Reduction Meets Message Passing for Graph Node EmbeddingsCode0
Spectral Clustering via Orthogonalization-Free MethodsCode0
Meta-learning of Pooling Layers for Character RecognitionCode0
Meta Reinforcement Learning with Finite Training Tasks -- a Density Estimation ApproachCode0
How to Evaluate Dimensionality Reduction? - Improving the Co-ranking MatrixCode0
Dimensionality Reduction for Improving Out-of-Distribution Detection in Medical Image SegmentationCode0
"Why Here and Not There?" -- Diverse Contrasting Explanations of Dimensionality ReductionCode0
A Fully Bayesian Gradient-Free Supervised Dimension Reduction Method using Gaussian ProcessesCode0
Dimensionality reduction of neuronal degeneracy reveals two interfering physiological mechanismsCode0
Robust PCA via Outlier PursuitCode0
Bidirectional deep-readout echo state networksCode0
Dimensionality reduction, regularization, and generalization in overparameterized regressionsCode0
Microstructure Representation and Reconstruction of Heterogeneous Materials via Deep Belief Network for Computational Material DesignCode0
Towards Exploratory Landscape Analysis for Large-scale Optimization: A Dimensionality Reduction FrameworkCode0
Unsupervised machine learning framework for discriminating major variants of concern during COVID-19Code0
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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