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 276–300 of 3304 papers

TitleStatusHype
ClusterGraph: a new tool for visualization and compression of multidimensional dataCode0
Firm Heterogeneity and Macroeconomic Fluctuations: a Functional VAR model—0
Poor Man's Training on MCUs: A Memory-Efficient Quantized Back-Propagation-Free Approach—0
On the Inherent Robustness of One-Stage Object Detection against Out-of-Distribution DataCode0
Theoretically informed selection of latent activation in autoencoder based recommender systems—0
Interpretable and Efficient Data-driven Discovery and Control of Distributed Systems—0
Understanding Contrastive Learning via Gaussian Mixture Models—0
You are out of context!—0
XNB: Explainable Class-Specific NaIve-Bayes ClassifierCode0
Detection and tracking of gas plumes in LWIR hyperspectral video sequence data—0
How Do Flow Matching Models Memorize and Generalize in Sample Data Subspaces?—0
Temporal Streaming Batch Principal Component Analysis for Time Series Classification—0
Dimension reduction via score ratio matching—0
Golden Ratio-Based Sufficient Dimension Reduction—0
DMT-HI: MOE-based Hyperbolic Interpretable Deep Manifold Transformation for Unspervised Dimensionality ReductionCode1
Enhancing Graph Attention Neural Network Performance for Marijuana Consumption Classification through Large-scale Augmented Granger Causality (lsAGC) Analysis of Functional MR Images—0
Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation—0
Doubly Non-Central Beta Matrix Factorization for Stable Dimensionality Reduction of Bounded Support Matrix Data—0
Enhancing literature review with LLM and NLP methods. Algorithmic trading case—0
Simultaneous Dimensionality Reduction for Extracting Useful Representations of Large Empirical Multimodal Datasets—0
A Wavelet Diffusion GAN for Image Super-Resolution—0
Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins—0
Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
A Kernelization-Based Approach to Nonparametric Binary Choice Models—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