SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 1551–1600 of 3304 papers

TitleStatusHype
SpaceMAP: Visualizing Any Data in 2-dimension by Space Expansion—0
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks—0
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions—0
An Efficient and Reliable Tolerance-Based Algorithm for Principal Component Analysis—0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based ROMs—0
Dimension Reduction for Data with Heterogeneous MissingnessCode0
Non-Euclidean Self-Organizing Maps—0
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications—0
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?Code0
Weighted Low Rank Matrix Approximation and Acceleration—0
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in ConnectomicsCode0
Probabilistic Bearing Fault Diagnosis Using Gaussian Process with Tailored Feature Extraction—0
Machine-Learned HASDM Model with Uncertainty Quantification—0
A Comparative Study of Machine Learning Methods for Predicting the Evolution of Brain Connectivity from a Baseline TimepointCode0
Disentangling Generative Factors of Physical Fields Using Variational Autoencoders—0
Concept Drift Detection in Federated Networked Systems—0
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons—0
On the use of Wasserstein metric in topological clustering of distributional data—0
Quality-Diversity Meta-Evolution: customising behaviour spaces to a meta-objectiveCode0
Quantum-Classical Hybrid Machine Learning for Image Classification (ICCAD Special Session Paper)—0
Detection of Epileptic Seizures on EEG Signals Using ANFIS Classifier, Autoencoders and Fuzzy Entropies—0
Ligand-induced protein dynamics differences correlate with protein-ligand binding affinities: An unsupervised deep learning approach—0
An Empirical Study on the Joint Impact of Feature Selection and Data Re-sampling on Imbalance Classification—0
Data-Driven Reduced-Order Modeling of Spatiotemporal Chaos with Neural Ordinary Differential Equations—0
Bubblewrap: Online tiling and real-time flow prediction on neural manifoldsCode0
Variational voxelwise rs-fMRI representation learning: Evaluation of sex, age, and neuropsychiatric signatures—0
Variational embedding of protein folding simulations using gaussian mixture variational autoencoders—0
Convolutional Autoencoders for Reduced-Order Modeling—0
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis—0
Convolutional Neural Networks Demystified: A Matched Filtering Perspective Based Tutorial—0
Uniform Manifold Approximation and Projection (UMAP) and its Variants: Tutorial and Survey—0
Multi-Criteria Radio Spectrum Sharing With Subspace-Based Pareto Tracing—0
Joint Characterization of Spatiotemporal Data Manifolds—0
Transformers predicting the future. Applying attention in next-frame and time series forecastingCode0
M-ar-K-Fast Independent Component AnalysisCode0
Disease-oriented image embedding with pseudo-scanner standardization for content-based image retrieval on 3D brain MRI—0
Dimensionality Reduction and State Space Systems: Forecasting the US Treasury Yields Using Frequentist and Bayesian VARs—0
Predicting Molecular Phenotypes with Single Cell RNA Sequencing Data: an Assessment of Unsupervised Machine Learning Models—0
ABC-FL: Anomalous and Benign client Classification in Federated Learning—0
Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey—0
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal EffectsCode0
A Hybrid Learning Approach to Detecting Regime Switches in Financial Markets—0
High dimensional Bayesian Optimization Algorithm for Complex System in Time Series—0
An iterative coordinate descent algorithm to compute sparse low-rank approximations—0
Efficient Multidimensional Functional Data Analysis Using Marginal Product Basis SystemsCode0
Machine Learning and Factor-Based Portfolio Optimization—0
Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey—0
Preliminary Steps Towards Federated Sentiment Classification—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