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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 501–525 of 3304 papers

TitleStatusHype
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
Golden Ratio-Based Sufficient Dimension Reduction—0
Dimension reduction via score ratio matching—0
Doubly Non-Central Beta Matrix Factorization for Stable Dimensionality Reduction of Bounded Support Matrix Data—0
Omics-driven hybrid dynamic modeling of bioprocesses with uncertainty estimation—0
Enhancing Graph Attention Neural Network Performance for Marijuana Consumption Classification through Large-scale Augmented Granger Causality (lsAGC) Analysis of Functional MR Images—0
A Wavelet Diffusion GAN for Image Super-Resolution—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
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
MIK: Modified Isolation Kernel for Biological Sequence Visualization, Classification, and Clustering—0
Lossless KV Cache Compression to 2%—0
Unsupervised feature selection algorithm framework based on neighborhood interval disturbance fusion—0
Efficient Sparse PCA via Block-Diagonalization—0
Game Theory Meets Statistical Mechanics in Deep Learning Design—0
Large data limits and scaling laws for tSNE—0
Approaching Metaheuristic Deep Learning Combos for Automated Data Mining—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