SOTAVerified

Dimensionality Reduction

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

( Image credit: openTSNE )

Papers

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Showing 576–600 of 3304 papers

TitleStatusHype
A Geometric take on Metric Learning—0
Abstraction and Symbolic Execution of Deep Neural Networks with Bayesian Approximation of Hidden Features—0
1-Bit Compressive Sensing for Efficient Federated Learning Over the Air—0
Compression-aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations—0
Computational Techniques in Multispectral Image Processing: Application to the Syriac Galen Palimpsest—0
Cone-Constrained Principal Component Analysis—0
A note on concentration inequality for vector-valued martingales with weak exponential-type tails—0
A Normative Theory of Adaptive Dimensionality Reduction in Neural Networks—0
A Generic Self-Supervised Framework of Learning Invariant Discriminative Features—0
A Normalized Bottleneck Distance on Persistence Diagrams and Homology Preservation under Dimension Reduction—0
A non-parametric conditional factor regression model for high-dimensional input and response—0
A Generative Model of Textures Using Hierarchical Probabilistic Principal Component Analysis—0
A convex formulation for high-dimensional sparse sliced inverse regression—0
A Nonlinear Dimensionality Reduction Framework Using Smooth Geodesics—0
Anomaly Subsequence Detection with Dynamic Local Density for Time Series—0
A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data—0
Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning System with Non-model Sharing Approach—0
Anomaly Detection Framework Using Rule Extraction for Efficient Intrusion Detection—0
A Generalized Mean Approach for Distributed-PCA—0
A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources—0
A Brief Survey on Representation Learning based Graph Dimensionality Reduction Techniques—0
An iterative coordinate descent algorithm to compute sparse low-rank approximations—0
An Item-Based Collaborative Filtering using Dimensionality Reduction Techniques on Mahout Framework—0
A generalized flow for multi-class and binary classification tasks: An Azure ML approach—0
An Investigation of Newton-Sketch and Subsampled Newton Methods—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