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Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 951–975 of 3304 papers

TitleStatusHype
Dimensionality Reduction in Sentence Transformer Vector Databases with Fast Fourier Transform—0
Automatic dimensionality reduction of Twin-in-the-Loop Observers—0
Dimension reduction and redundancy removal through successive Schmidt decompositions—0
Stochastic neighborhood embedding and the gradient flow of relative entropy—0
Clustering and Recognition of Spatiotemporal Features through Interpretable Embedding of Sequence to Sequence Recurrent Neural Networks—0
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification—0
Dimension-Reduction Attack! Video Generative Models are Experts on Controllable Image Synthesis—0
Dimensionality Reduction in Deep Learning for Chest X-Ray Analysis of Lung Cancer—0
Dimension reduction for derivative-informed operator learning: An analysis of approximation errors—0
Dimension Reduction for Efficient Data-Enabled Predictive Control—0
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds—0
Automatic Collection Creation and Recommendation—0
Dimensionality Reduction for Wasserstein Barycenter—0
Dimensionality Reduction for Tukey Regression—0
Automated Disease Normalization with Low Rank Approximations—0
Dimension reduction in recurrent networks by canonicalization—0
A Multimodal Intermediate Fusion Network with Manifold Learning for Stress Detection—0
Dimensionality reduction for time series data—0
Dimension reduction of open-high-low-close data in candlestick chart based on pseudo-PCA—0
Dimension Reduction Using Active Manifolds—0
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization—0
Dimension reduction via score ratio matching—0
Dimension Reduction via Sum-of-Squares and Improved Clustering Algorithms for Non-Spherical Mixtures—0
Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis—0
Dimensionality Reduction for Sentiment Classification: Evolving for the Most Prominent and Separable Features—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