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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 2476–2500 of 3304 papers

TitleStatusHype
Non-linear dimensionality reduction: Riemannian metric estimation and the problem of geometric discovery—0
Nonlinear Dimensionality Reduction for Discriminative Analytics of Multiple Datasets—0
Nonlinear Dimensionality Reduction for Data Visualization: An Unsupervised Fuzzy Rule-based Approach—0
Nonlinear Dimensionality Reduction on Graphs—0
Nonlinear dimensionality reduction then and now: AIMs for dissipative PDEs in the ML era—0
Non-linear dimension reduction in factor-augmented vector autoregressions—0
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions—0
Nonlinear Feature Aggregation: Two Algorithms driven by Theory—0
Nonlinear functional regression by functional deep neural network with kernel embedding—0
Nonlinear Manifold Learning Determines Microgel Size from Raman Spectroscopy—0
Non-Linear Spectral Dimensionality Reduction Under Uncertainty—0
Nonlinear Structural Vector Autoregressive Models for Inferring Effective Brain Network Connectivity—0
Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network—0
Nonlinear Supervised Dimensionality Reduction via Smooth Regular Embeddings—0
Nonnegative Matrix Factorization in Dimensionality Reduction: A Survey—0
Non-Negative Matrix Factorization with Scale Data Structure Preservation—0
Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction—0
Non-negative Subspace Feature Representation for Few-shot Learning in Medical Imaging—0
Non-Orthogonal Explicit Semantic Analysis—0
Nonparametric Bellman Mappings for Reinforcement Learning: Application to Robust Adaptive Filtering—0
Non-PSD Matrix Sketching with Applications to Regression and Optimization—0
Non-Redundant Spectral Dimensionality Reduction—0
Nonsmoothness in Machine Learning: specific structure, proximal identification, and applications—0
Non-Volatile Memory Accelerated Geometric Multi-Scale Resolution Analysis—0
Notes on Low-rank Matrix Factorization—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