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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 1626–1650 of 3304 papers

TitleStatusHype
On the Whitney near extension problem, BMO, alignment of data, best approximation in algebraic geometry, manifold learning and their beautiful connections: A modern treatmentCode0
Unsupervised Anomaly Segmentation using Image-Semantic Cycle Translation—0
Data Discovery Using Lossless Compression-Based Sparse Representation—0
Revisiting Dynamic Convolution via Matrix DecompositionCode1
R-PointHop: A Green, Accurate, and Unsupervised Point Cloud Registration MethodCode1
Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation—0
DefakeHop: A Light-Weight High-Performance Deepfake DetectorCode1
Modern Dimension ReductionCode1
A Local Similarity-Preserving Framework for Nonlinear Dimensionality Reduction with Neural Networks—0
BIKED: A Dataset for Computational Bicycle Design with Machine Learning BenchmarksCode1
Explaining dimensionality reduction results using Shapley valuesCode1
Simplicial RegularizationCode0
Grassmann Graph Embedding—0
Empirical comparison between autoencoders and traditional dimensionality reduction methods—0
Low-Rank Isomap Algorithm—0
Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features—0
SCALE SPACE FLOW WITH AUTOREGRESSIVE PRIORS—0
Hard-label Manifolds: Unexpected Advantages of Query Efficiency for Finding On-manifold Adversarial Examples—0
Lazy FSCA for Unsupervised Variable SelectionCode0
Minimum-Distortion EmbeddingCode1
FedPower: Privacy-Preserving Distributed Eigenspace Estimation—0
The Mathematics Behind Spectral Clustering And The Equivalence To PCA—0
Unsupervised dynamic modeling of medical image transformationCode0
Validating Clustering Frameworks for Electric Load Demand ProfilesCode0
Detection of Alzheimer's Disease Using Graph-Regularized Convolutional Neural Network Based on Structural Similarity Learning of Brain Magnetic Resonance Images—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