SOTAVerified

Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1701–1750 of 3304 papers

TitleStatusHype
Classification of Schizophrenia from Functional MRI Using Large-scale Extended Granger Causality—0
Data augmentation and feature selection for automatic model recommendation in computational physics—0
Towards glass-box CNNs—0
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks—0
Large-scale Augmented Granger Causality (lsAGC) for Connectivity Analysis in Complex Systems: From Computer Simulations to Functional MRI (fMRI)—0
Curvature-based Feature Selection with Application in Classifying Electronic Health RecordsCode1
Smile and Laugh Expressions Detection Based on Local Minimum Key Points—0
Order Embeddings from Merged Ontologies using Sketching—0
Large-Scale Extended Granger Causality for Classification of Marijuana Users From Functional MRI—0
A Linearly Convergent Algorithm for Distributed Principal Component AnalysisCode0
Analyzing movies to predict their commercial viability for producers—0
Protecting Big Data Privacy Using Randomized Tensor Network Decomposition and Dispersed Tensor Computation—0
Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey—0
A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data—0
Aha! Adaptive History-Driven Attack for Decision-Based Black-Box ModelsCode1
Deep Manifold Computing and Visualization Using Elastic Locally Isometric Smoothness—0
Selective Sensing: A Data-driven Nonuniform Subsampling Approach for Computation-free On-Sensor Data Dimensionality Reduction—0
Graph Neural Network Acceleration via Matrix Dimension Reduction—0
Graph Learning via Spectral Densification—0
On the Importance of Distraction-Robust Representations for Robot Learning—0
Divergence Regulated Encoder Network for Joint Dimensionality Reduction and ClassificationCode0
A Memory Efficient Baseline for Open Domain Question AnsweringCode1
Stochastic Approximation for Online Tensorial Independent Component Analysis—0
Manifold learning with arbitrary normsCode0
A method to integrate and classify normal distributionsCode0
Unsupervised Functional Data Analysis via Nonlinear Dimension ReductionCode0
Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement LearningCode0
Exploiting Vulnerability of Pooling in Convolutional Neural Networks by Strict Layer-Output Manipulation for Adversarial Attacks—0
Upper and Lower Bounds on the Performance of Kernel PCA—0
Instance Space Analysis for the Car Sequencing Problem—0
SRoll3: A neural network approach to reduce large-scale systematic effects in the Planck High Frequency Instrument maps—0
Difficulty in estimating visual information from randomly sampled images—0
Real-time Inflation Forecasting Using Non-linear Dimension Reduction Techniques—0
A t-SNE Based Classification Approach to Compositional Microbiome Data—0
Probabilistic Contrastive Principal Component AnalysisCode1
Recovery of Linear Components: Reduced Complexity Autoencoder Designs—0
Clustering high dimensional meteorological scenarios: results and performance index—0
Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation—0
Generating semantic maps through multidimensional scaling: linguistic applications and theory—0
Spatial noise-aware temperature retrieval from infrared sounder data—0
Understanding How Dimension Reduction Tools Work: An Empirical Approach to Deciphering t-SNE, UMAP, TriMAP, and PaCMAP for Data VisualizationCode1
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorizationCode0
Out-Of-Distribution Detection With Subspace Techniques And Probabilistic Modeling Of Features—0
Multitask machine learning of collective variables for enhanced sampling of rare events—0
Data-driven Model Predictive Control Method for DFIG-based Wind Farm to Provide Primary Frequency Regulation Service—0
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare—0
A journey in ESN and LSTM visualisations on a language taskCode0
K-Deep Simplex: Deep Manifold Learning via Local DictionariesCode0
Compressive Sensing Approaches for Sparse Distribution Estimation Under Local Privacy—0
q-SNE: Visualizing Data using q-Gaussian Distributed Stochastic Neighbor EmbeddingCode0
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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