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Dimensionality Reduction

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

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Papers

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Showing 2201–2225 of 3304 papers

TitleStatusHype
Text Clustering with Large Language Model Embeddings—0
Text comparison using word vector representations and dimensionality reduction—0
FastSVD-ML-ROM: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications—0
The AV-LASYN Database : A synchronous corpus of audio and 3D facial marker data for audio-visual laughter synthesis—0
Evolution is Driven by Natural Autoencoding: Reframing Species, Interaction Codes, Cooperation, and Sexual Reproduction—0
The constitution of visual perceptual units in the functional architecture of V1—0
The Dilemma Between Data Transformations and Adversarial Robustness for Time Series Application Systems—0
The Diversity Metrics of Sub-models based on SVD of Jacobians for Ensembles Adversarial Robustness—0
The Dynamical Gaussian Process Latent Variable Model in the Longitudinal Scenario—0
The Effectiveness of Johnson-Lindenstrauss Transform for High Dimensional Optimization With Adversarial Outliers, and the Recovery—0
The equivalence of information-theoretic and likelihood-based methods for neural dimensionality reduction—0
The face-space duality hypothesis: a computational model—0
The Forecasting performance of the Factor model with Martingale Difference errors—0
The G-invariant graph Laplacian—0
The Information Bottleneck Problem and Its Applications in Machine Learning—0
The Informativeness of K -Means for Learning Mixture Models—0
The intrinsic value of HFO features as a biomarker of epileptic activity—0
The Mathematical Foundations of Manifold Learning—0
The Mathematics Behind Spectral Clustering And The Equivalence To PCA—0
NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation—0
Theoretical Connection between Locally Linear Embedding, Factor Analysis, and Probabilistic PCA—0
Theoretical Foundations of t-SNE for Visualizing High-Dimensional Clustered Data—0
Theoretical Guarantees for Sparse Principal Component Analysis based on the Elastic Net—0
Theoretical Insights into the Use of Structural Similarity Index In Generative Models and Inferential Autoencoders—0
Theoretically informed selection of latent activation in autoencoder based recommender systems—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