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Dimensionality Reduction

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

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Papers

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Showing 1626–1650 of 3304 papers

TitleStatusHype
Bounds on Causal Effects and Application to High Dimensional Data—0
3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching—0
Objective discovery of dominant dynamical processes with intelligible machine learningCode0
Low-rank Characteristic Tensor Density Estimation Part II: Compression and Latent Density EstimationCode0
Topological Indoor Mapping through WiFi Signals—0
On Effects of Compression with Hyperdimensional Computing in Distributed Randomized Neural Networks—0
Non-PSD Matrix Sketching with Applications to Regression and Optimization—0
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification—0
Reproducing Kernel Hilbert Space, Mercer's Theorem, Eigenfunctions, Nyström Method, and Use of Kernels in Machine Learning: Tutorial and Survey—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
Discovering Interpretable Machine Learning Models in Parallel Coordinates—0
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
Sirius: Visualization of Mixed Features as a Mutual Information Network GraphCode0
Evaluating Meta-Feature Selection for the Algorithm Recommendation ProblemCode0
Neural dSCA: demixing multimodal interaction among brain areas during naturalistic experiments—0
A Discussion On the Validity of Manifold Learning—0
A Subspace-based Approach for Dimensionality Reduction and Important Variable Selection—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
Matrix factorisation and the interpretation of geodesic distanceCode0
Wireless Federated Learning with Limited Communication and Differential Privacy—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