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Dimensionality Reduction

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

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Papers

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Showing 2501–2525 of 3304 papers

TitleStatusHype
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets—0
TRAPACC and TRAPACCS at PARSEME Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions—0
Neural Activation Semantic Models: Computational lexical semantic models of localized neural activationsCode0
Model-Free Context-Aware Word Composition—0
t-SNE-CUDA: GPU-Accelerated t-SNE and its Applications to Modern DataCode0
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas—0
Dynamical Component Analysis (DyCA): Dimensionality Reduction For High-Dimensional Deterministic Time-Series—0
Premise selection with neural networks and distributed representation of featuresCode0
Learning low dimensional word based linear classifiers using Data Shared Adaptive Bootstrap Aggregated Lasso with application to IMDb data—0
Prototype Discovery using Quality-Diversity—0
Multi-view Reconstructive Preserving Embedding for Dimension Reduction—0
Space-Time Extension of the MEM Approach for Electromagnetic Neuroimaging—0
A Generative-Discriminative Basis Learning Framework to Predict Clinical Severity from Resting State Functional MRI Data—0
Recurrent Neural Networks for Long and Short-Term Sequential Recommendation—0
Tree-structured multi-stage principal component analysis (TMPCA): theory and applications—0
A Trace Lasso Regularized L1-norm Graph Cut for Highly Correlated Noisy Hyperspectral Image—0
Isolation Kernel and Its Effect on SVM—0
Unsupervised Metric Learning in Presence of Missing DataCode0
Parametric generation of conditional geological realizations using generative neural networksCode0
Channel Charting: Locating Users within the Radio Environment using Channel State Information—0
Non-Gaussian Component Analysis using Entropy Methods—0
Structured Bayesian Gaussian process latent variable model: applications to data-driven dimensionality reduction and high-dimensional inversionCode0
A GPU-Oriented Algorithm Design for Secant-Based Dimensionality Reduction—0
A Supervised Geometry-Aware Mapping Approach for Classification of Hyperspectral Images—0
Learning Low-Dimensional Temporal Representations—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