SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2431–2440 of 3304 papers

TitleStatusHype
Weight Vector Tuning and Asymptotic Analysis of Binary Linear Classifiers—0
Wheat Crop Yield Prediction Using Deep LSTM Model—0
When Collaborative Filtering is not Collaborative: Unfairness of PCA for Recommendations—0
When Dimensionality Reduction Meets Graph (Drawing) Theory: Introducing a Common Framework, Challenges and Opportunities—0
Where do goals come from? A Generic Approach to Autonomous Goal-System Development—0
Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?—0
Why do universal adversarial attacks work on large language models?: Geometry might be the answer—0
Wireless Channel Charting: Theory, Practice, and Applications—0
Wireless Environment Information Sensing, Feature, Semantic, and Knowledge: Four Steps Towards 6G AI-Enabled Air Interface—0
Wireless Federated Learning with Limited Communication and Differential Privacy—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