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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 901–950 of 3304 papers

TitleStatusHype
A VAE-Bayesian Deep Learning Scheme for Solar Generation Forecasting based on Dimensionality Reduction—0
Auto-weighted Mutli-view Sparse Reconstructive Embedding—0
AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images—0
An Adaptive Dimension Reduction Estimation Method for High-dimensional Bayesian Optimization—0
A Deep Signed Directional Distance Function for Object Shape Representation—0
A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering—0
Dimensionality Reduction via Diffusion Map Improved with Supervised Linear Projection—0
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents—0
ELBD: Efficient score algorithm for feature selection on latent variables of VAE—0
Autonomous Dimension Reduction by Flattening Deformation of Data Manifold under an Intrinsic Deforming Field—0
Dimensionality reduction: theoretical perspective on practical measures—0
Autonomous Collaborative Scheduling of Time-dependent UAVs, Workers and Vehicles for Crowdsensing in Disaster Response—0
An adaptive block based integrated LDP,GLCM,and Morphological features for Face Recognition—0
Dimensionality Reduction Techniques for Global Bayesian Optimisation—0
Automatic Selection of t-SNE Perplexity—0
An Accelerated Doubly Stochastic Gradient Method with Faster Explicit Model Identification—0
Dimensionality Reduction on Grassmannian via Riemannian Optimization: A Generalized Perspective—0
Automatic Prediction of the Performance of Every Parser—0
A Music Classification Model based on Metric Learning and Feature Extraction from MP3 Audio Files—0
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017—0
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation—0
3D Shape Registration Using Spectral Graph Embedding and Probabilistic Matching—0
Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning—0
Automatic Debiased Estimation with Machine Learning-Generated Regressors—0
Dimensionality Reduction of Collective Motion by Principal Manifolds—0
Dimensionality Reduction of Affine Variational Inequalities Using Random Projections—0
Dimensionality Reduction of Massive Sparse Datasets Using Coresets—0
Dimensionality Reduction of Movement Primitives in Parameter Space—0
Automatic Double Reinforcement Learning in Semiparametric Markov Decision Processes with Applications to Long-Term Causal Inference—0
A Multi-view Dimensionality Reduction Algorithm Based on Smooth Representation Model—0
Dimensionality Reduction on SPD Manifolds: The Emergence of Geometry-Aware Methods—0
Dimensionality reduction methods for molecular simulations—0
Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform—0
Dimensionality reduction techniques to support insider trading detection—0
Automatic dimensionality reduction of Twin-in-the-Loop Observers—0
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification—0
Dimensionality Reduction using Elastic Measures—0
Dimensionality Reduction Using pseudo-Boolean polynomials For Cluster Analysis—0
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer—0
Dimensionality Reduction Using the Sparse Linear Model—0
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds—0
Dimensionality reduction via path integration for computing mRNA distributions—0
Dimensionality Reduction via Regression in Hyperspectral Imagery—0
Dimensionality reduction with missing values imputation—0
Dimensionality reduction with subgaussian matrices: a unified theory—0
Dimensionality Reduction with Subspace Structure Preservation—0
Automatic Collection Creation and Recommendation—0
Dimension Estimation Using Random Connection Models—0
Dimensionality Reduction for Wasserstein Barycenter—0
Dimensionality Reduction for Tukey Regression—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