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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 1001–1050 of 3304 papers

TitleStatusHype
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
On Sufficient Graphical Models—0
Functional PCA and Deep Neural Networks-based Bayesian Inverse Uncertainty Quantification with Transient Experimental Data—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
Distance Preserving Machine Learning for Uncertainty Aware Accelerator Capacitance Predictions—0
Wasserstein Auto-Encoders of Merge Trees (and Persistence Diagrams)—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
Lightweight Modeling of User Context Combining Physical and Virtual Sensor Data—0
Enhanced Neural Beamformer with Spatial Information for Target Speech Extraction—0
Feature Selection: A perspective on inter-attribute cooperation—0
Emulating the dynamics of complex systems using autoregressive models on manifolds (mNARX)—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
On the use of the Gram matrix for multivariate functional principal components analysisCode0
Efficient Solution of Portfolio Optimization Problems via Dimension Reduction and SparsificationCode0
Relating tSNE and UMAP to Classical Dimensionality Reduction—0
Nonlinear Feature Aggregation: Two Algorithms driven by Theory—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
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
Bayesian Non-linear Latent Variable Modeling via Random Fourier FeaturesCode0
Fault Detection in Induction Motors using Functional Dimensionality Reduction Methods—0
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
The Rank-Reduced Kalman Filter: Approximate Dynamical-Low-Rank Filtering In High DimensionsCode0
On building machine learning pipelines for Android malware detection: a procedural survey of practices, challenges and opportunities—0
G-invariant diffusion maps—0
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula—0
A Normalized Bottleneck Distance on Persistence Diagrams and Homology Preservation under Dimension Reduction—0
Differentially private sliced inverse regression in the federated paradigm—0
PLPCA: Persistent Laplacian Enhanced-PCA for Microarray Data AnalysisCode0
Yet Another Algorithm for Supervised Principal Component Analysis: Supervised Linear Centroid-Encoder—0
Multiscale Flow for Robust and Optimal Cosmological Analysis—0
Identifying Shared Decodable Concepts in the Human Brain Using Image-Language Foundation Models—0
SDR-GAIN: A High Real-Time Occluded Pedestrian Pose Completion Method for Autonomous Driving—0
Prescriptive PCA: Dimensionality Reduction for Two-stage Stochastic Optimization—0
Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization—0
ShaRP: Shape-Regularized Multidimensional ProjectionsCode0
Dictionary Learning under Symmetries via Group Representations—0
Label Embedding via Low-Coherence Matrices—0
Dimensionality Reduction for General KDE Mode Finding—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