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Dimensionality Reduction

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

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Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1271–1280 of 3304 papers

TitleStatusHype
Simple and Powerful Architecture for Inductive Recommendation Using Knowledge Graph Convolutions—0
Dimensionality Reduction using Elastic Measures—0
Application of advanced machine learning algorithms for anomaly detection and quantitative prediction in protein A chromatography—0
Learning Canonical Embeddings for Unsupervised Shape Correspondence with Locally Linear Transformations—0
Johnson-Lindenstrauss embeddings for noisy vectors -- taking advantage of the noise—0
Practical Operator Sketching Framework for Accelerating Iterative Data-Driven Solutions in Inverse Problems—0
Identifying Dominant Industrial Sectors in Market States of the S&P 500 Financial Data—0
Embedding Functional Data: Multidimensional Scaling and Manifold Learning—0
Affective Manifolds: Modeling Machine's Mind to Like, Dislike, Enjoy, Suffer, Worry, Fear, and Feel Like A HumanCode0
AutoQML: Automatic Generation and Training of Robust Quantum-Inspired Classifiers by Using Genetic Algorithms on Grayscale Images—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