SOTAVerified

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 1476–1500 of 3304 papers

TitleStatusHype
ABC-FL: Anomalous and Benign client Classification in Federated Learning—0
Johnson-Lindenstrauss Lemma, Linear and Nonlinear Random Projections, Random Fourier Features, and Random Kitchen Sinks: Tutorial and Survey—0
Identifiable Energy-based Representations: An Application to Estimating Heterogeneous Causal EffectsCode0
A Hybrid Learning Approach to Detecting Regime Switches in Financial Markets—0
High dimensional Bayesian Optimization Algorithm for Complex System in Time Series—0
Discovering Distinctive "Semantics" in Super-Resolution NetworksCode1
An iterative coordinate descent algorithm to compute sparse low-rank approximations—0
Efficient Multidimensional Functional Data Analysis Using Marginal Product Basis SystemsCode0
Machine Learning and Factor-Based Portfolio Optimization—0
Restricted Boltzmann Machine and Deep Belief Network: Tutorial and Survey—0
Preliminary Steps Towards Federated Sentiment Classification—0
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
A Deep Signed Directional Distance Function for Object Shape Representation—0
A local approach to parameter space reduction for regression and classification tasksCode1
Segmentation of Cardiac Structures via Successive Subspace Learning with Saab Transform from Cine MRI—0
Deep Adaptive Arbitrary Polynomial Chaos Expansion: A Mini-data-driven Semi-supervised Method for Uncertainty QuantificationCode0
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate modelsCode1
Machine learning for assessing quality of service in the hospitality sector based on customer reviews—0
Measuring inter-cluster similarities with Alpha Shape TRIangulation in loCal Subspaces (ASTRICS) facilitates visualization and clustering of high-dimensional data—0
Optimality of the Johnson-Lindenstrauss Dimensionality Reduction for Practical Measures—0
Identifying Layers Susceptible to Adversarial Attacks—0
Deep Learning for Reduced Order Modelling and Efficient Temporal Evolution of Fluid SimulationsCode1
On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit—0
WeightScale: Interpreting Weight Change in Neural Networks—0
Generative locally linear embedding: A module for manifold unfolding and visualizationCode1
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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