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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 326–350 of 3304 papers

TitleStatusHype
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—0
A Lightweight ReLU-Based Feature Fusion for Aerial Scene Classification—0
Co-regularized Multi-view Sparse Reconstruction Embedding for Dimension Reduction—0
A Light weight and Hybrid Deep Learning Model based Online Signature Verification—0
Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning—0
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees)—0
A Stable Measure for Conditional Periodicity of Time Series using Persistent Homology—0
Asteroids co-orbital motion classification based on Machine Learning—0
Accelerating hyperbolic t-SNE—0
Algorithms for Approximate Subtropical Matrix Factorization—0
2D+3D facial expression recognition via embedded tensor manifold regularization—0
Algorithmic Stability and Uniform Generalization—0
Algorithm-Agnostic Interpretations for Clustering—0
Adaptive Metric Dimensionality Reduction—0
A Latent Variable Model for Two-Dimensional Canonical Correlation Analysis and its Variational Inference—0
Adaptive Locally Linear Embedding—0
Deep Learning Reveals Underlying Physics of Light-matter Interactions in Nanophotonic Devices—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
A Large Scale Evaluation of Distributional Semantic Models: Parameters, Interactions and Model Selection—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
A Spatial Mapping Algorithm with Applications in Deep Learning-Based Structure Classification—0
Split Semantic Detection in Sandplay Images—0
AstroM^3: A self-supervised multimodal model for astronomy—0
A Supervised Tensor Dimension Reduction-Based Prognostics Model for Applications with Incomplete Imaging Data—0
A theoretical contribution to the fast implementation of null linear discriminant analysis method using random matrix multiplication with scatter matrices—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