SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 1501–1550 of 3304 papers

TitleStatusHype
Finding Significant Features for Few-Shot Learning using Dimensionality Reduction—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
fMBN-E: Efficient Unsupervised Network Structure Ensemble and Selection for Clustering—0
Concept Identification of Directly and Indirectly Related Mentions Referring to Groups of Persons—0
Unified Framework for Spectral Dimensionality Reduction, Maximum Variance Unfolding, and Kernel Learning By Semidefinite Programming: Tutorial and Survey—0
Interactive Dimensionality Reduction for Comparative AnalysisCode0
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
Efficient Tensor Contraction via Fast Count Sketch—0
Regularisation for PCA- and SVD-type matrix factorisations—0
Partial Maximum Correntropy Regression for Robust Trajectory Decoding from Noisy Epidural Electrocorticographic Signals—0
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case study—0
Bounds on Causal Effects and Application to High Dimensional Data—0
Objective discovery of dominant dynamical processes with intelligible machine learningCode0
3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching—0
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density EstimationCode0
SKFAC: Training Neural Networks With Faster Kronecker-Factored Approximate CurvatureCode1
Deep Learning for Functional Data Analysis with Adaptive Basis LayersCode1
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks—0
PyKale: Knowledge-Aware Machine Learning from Multiple Sources in PythonCode1
Topological Indoor Mapping through WiFi Signals—0
Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICACode1
Non-PSD Matrix Sketching with Applications to Regression and Optimization—0
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey—0
ATD: Augmenting CP Tensor Decomposition by Self SupervisionCode1
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification—0
Graphical Gaussian Process Regression Model for Aqueous Solvation Free Energy Prediction of Organic Molecules in Redox Flow Battery—0
Computer-aided Interpretable Features for Leaf Image ClassificationCode0
HUMAP: Hierarchical Uniform Manifold Approximation and ProjectionCode1
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
Improving Metric Dimensionality Reduction with Distributed TopologyCode1
Quantum diffusion map for nonlinear dimensionality reduction—0
Distributionally Robust Optimization with Markovian DataCode0
Quantifying the Conceptual Error in Dimensionality Reduction—0
Real-time simulation of parameter-dependent fluid flows through deep learning-based reduced order models—0
Unsupervised Behaviour Discovery with Quality-Diversity OptimisationCode1
Large-scale optimal transport map estimation using projection pursuitCode1
Sirius: Visualization of Mixed Features as a Mutual Information Network GraphCode0
Scalable conditional deep inverse Rosenblatt transports using tensor-trains and gradient-based dimension reductionCode1
HoroPCA: Hyperbolic Dimensionality Reduction via Horospherical ProjectionsCode1
Evaluating Meta-Feature Selection for the Algorithm Recommendation ProblemCode0
Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments—0
Laplacian-Based Dimensionality Reduction Including Spectral Clustering, Laplacian Eigenmap, Locality Preserving Projection, Graph Embedding, and Diffusion Map: Tutorial and Survey—0
Shape-Preserving Dimensionality Reduction : An Algorithm and Measures of Topological Equivalence—0
A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection—0
A Discussion On the Validity of Manifold Learning—0
Matrix factorisation and the interpretation of geodesic distanceCode0
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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