SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 2226–2250 of 3304 papers

TitleStatusHype
Theoretical Understandings of Product Embedding for E-commerce Machine Learning—0
The Perfect Marriage and Much More: Combining Dimension Reduction, Distance Measures and Covariance—0
The Potential of Quantum Techniques for Stock Price Prediction—0
The Powerful Use of AI in the Energy Sector: Intelligent Forecasting—0
The Power of Typed Affine Decision Structures: A Case Study—0
Thermal hand image segmentation for biometric recognition—0
Thermal Human face recognition based on Haar wavelet transform and series matching technique—0
The role of dimensionality reduction in linear classification—0
The R package psvmSDR: A Unified Algorithm for Sufficient Dimension Reduction via Principal Machines—0
The Similarity-Consensus Regularized Multi-view Learning for Dimension Reduction—0
The space complexity of inner product filters—0
Determining Principal Component Cardinality through the Principle of Minimum Description Length—0
The Surprising Robustness of Partial Least Squares—0
EvolvED: Evolutionary Embeddings to Understand the Generation Process of Diffusion Models—0
The Underlying Correlated Dynamics in Neural Training—0
Thinking Outside the Box: Orthogonal Approach to Equalizing Protected Attributes—0
This also affects the context - Errors in extraction based summaries—0
T- Hop: Tensor representation of paths in graph convolutional networks—0
Three-body renormalization group limit cycles based on unsupervised feature learning—0
Threshold Strategy for Leaking Corner-Free Hamilton-Jacobi Reachability with Decomposed Computations—0
Tight bounds for learning a mixture of two gaussians—0
Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels—0
Time delay multi-feature correlation analysis to extract subtle dependencies from EEG signals—0
Time-Efficient Reward Learning via Visually Assisted Cluster Ranking—0
Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics—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