SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 826–850 of 3304 papers

TitleStatusHype
Learning Environment Models with Continuous Stochastic Dynamics—0
Long-term Conversation Analysis: Exploring Utility and PrivacyCode0
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)—0
Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data—0
Feature Selection: A perspective on inter-attribute cooperation—0
Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction—0
Learning Nonautonomous Systems via Dynamic Mode Decomposition—0
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs—0
Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNECode0
Factor-augmented sparse MIDAS regressions with an application to nowcasting—0
Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and SparsificationCode0
On the use of the Gram matrix for multivariate functional principal components analysisCode0
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequencesCode1
Relating tSNE and UMAP to Classical Dimensionality Reduction—0
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)Code0
Application of Deep Learning for Predictive Maintenance of Oilfield Equipment—0
Nonlinear Feature Aggregation: Two Algorithms driven by Theory—0
Vision Transformer with Attention Map Hallucination and FFN Compaction—0
Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction—0
Enhanced Sampling with Machine Learning: A Review—0
Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods—0
Bayesian Non-linear Latent Variable Modeling via Random Fourier FeaturesCode0
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High DimensionsCode0
On Selecting Distance Metrics in n-Dimensional Normed Vector Spaces of Cells: A Novel Criterion and Similarity Measure Towards Efficient and Accurate Omics Analysis—0
G-invariant diffusion maps—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