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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2126–2150 of 3304 papers

TitleStatusHype
Isolation Kernel and Its Effect on SVM—0
Isometric Autoencoders—0
Isometric sketching of any set via the Restricted Isometry Property—0
Is 'Unsupervised Learning' a Misconceived Term?—0
Iterated Relevance Matrix Analysis (IRMA) for the identification of class-discriminative subspaces—0
Iterative Cluster Harvesting for Wafer Map Defect Patterns—0
IT-map: an Effective Nonlinear Dimensionality Reduction Method for Interactive Clustering—0
Statistical feature embedding for heart sound classification—0
Jaeger: A Concatenation-Based Multi-Transformer VQA Model—0
Johnson-Lindenstrauss embeddings for noisy vectors -- taking advantage of the noise—0
Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey—0
Unraveling the Veil of Subspace RIP Through Near-Isometry on Subspaces—0
Joint Characterization of Multiscale Information in High Dimensional Data—0
Joint Characterization of Spatiotemporal Data Manifolds—0
Joint Characterization of the Cryospheric Spectral Feature Space—0
Joint Dimensionality Reduction and Metric Learning: A Geometric Take—0
Joint Dimensionality Reduction for Separable Embedding Estimation—0
Joint Dimensionality Reduction for Two Feature Vectors—0
Jointly Event Extraction and Visualization on Twitter via Probabilistic Modelling—0
Joint Projection and Dictionary Learning using Low-rank Regularization and Graph Constraints—0
Joint Semi-supervised RSS Dimensionality Reduction and Fingerprint Based Algorithm for Indoor Localization—0
Kernel methods for detecting coherent structures in dynamical data—0
Kernelized Multiview Subspace Analysis by Self-weighted Learning—0
Kernel Methods for the Approximation of Nonlinear Systems—0
Kernel Multivariate Analysis Framework for Supervised Subspace Learning: A Tutorial on Linear and Kernel Multivariate Methods—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