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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 801–850 of 3304 papers

TitleStatusHype
Decomposing and Coupling Saliency Map for Lesion Segmentation in Ultrasound Images—0
ZADU: A Python Library for Evaluating the Reliability of Dimensionality Reduction EmbeddingsCode1
Classes are not Clusters: Improving Label-based Evaluation of Dimensionality ReductionCode0
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels—0
SAP-sLDA: An Interpretable Interface for Exploring Unstructured Text—0
A Statistical View of Column Subset SelectionCode0
On Optimality in ROVir—0
Heuristic Hyperparameter Choice for Image Anomaly Detection—0
Two Approaches to Supervised Image Segmentation—0
A Computational Topology-based Spatiotemporal Analysis Technique for Honeybee AggregationCode0
Improving Surrogate Model Robustness to Perturbations for Dynamical Systems Through Machine Learning and Data Assimilation—0
Extreme heatwave sampling and prediction with analog Markov chain and comparisons with deep learning—0
Company2Vec -- German Company Embeddings based on Corporate Websites—0
Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text SpatializationCode0
Benchmarking the Effectiveness of Classification Algorithms and SVM Kernels for Dry Beans—0
Learning Active Subspaces and Discovering Important Features with Gaussian Radial Basis Functions Neural NetworksCode0
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data—0
On Sufficient Graphical Models—0
Bayesian tomography using polynomial chaos expansion and deep generative networks—0
Differential Privacy for Clustering Under Continual Observation—0
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value RegularizationCode0
Principal subbundles for dimension reduction—0
Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams)—0
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions—0
Supervised Manifold Learning via Random Forest Geometry-Preserving Proximities—0
Learning Environment Models with Continuous Stochastic Dynamics—0
Long-term Conversation Analysis: Exploring Utility and PrivacyCode0
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)—0
Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data—0
Feature Selection: A perspective on inter-attribute cooperation—0
Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction—0
Learning Nonautonomous Systems via Dynamic Mode Decomposition—0
Enhancing Representation Learning on High-Dimensional, Small-Size Tabular Data: A Divide and Conquer Method with Ensembled VAEs—0
Analyzing scRNA-seq data by CCP-assisted UMAP and t-SNECode0
Factor-augmented sparse MIDAS regressions with an application to nowcasting—0
Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and SparsificationCode0
On the use of the Gram matrix for multivariate functional principal components analysisCode0
DIAS: A Dataset and Benchmark for Intracranial Artery Segmentation in DSA sequencesCode1
Relating tSNE and UMAP to Classical Dimensionality Reduction—0
Introduction to Facial Micro Expressions Analysis Using Color and Depth Images: A Matlab Coding Approach (Second Edition, 2023)Code0
Application of Deep Learning for Predictive Maintenance of Oilfield Equipment—0
Nonlinear Feature Aggregation: Two Algorithms driven by Theory—0
Vision Transformer with Attention Map Hallucination and FFN Compaction—0
Linearly-scalable learning of smooth low-dimensional patterns with permutation-aided entropic dimension reduction—0
Enhanced Sampling with Machine Learning: A Review—0
Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods—0
Bayesian Non-linear Latent Variable Modeling via Random Fourier FeaturesCode0
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High DimensionsCode0
On Selecting Distance Metrics in n-Dimensional Normed Vector Spaces of Cells: A Novel Criterion and Similarity Measure Towards Efficient and Accurate Omics Analysis—0
G-invariant diffusion maps—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