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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 2401–2450 of 3304 papers

TitleStatusHype
A Low Effort Approach to Structured CNN Design Using PCA—0
Extending classical surrogate modelling to high-dimensions through supervised dimensionality reduction: a data-driven approach—0
Class Mean Vector Component and Discriminant Analysis—0
Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach—0
A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms, Experimental Analysis, Prospects and Challenges (with Appendices on Mathematical Background and Detailed Algorithms Explanation)—0
Graph Signal Representation with Wasserstein Barycenters—0
Classification of Cervical Cancer Dataset—0
Multi-Dimensional Scaling on Groups—0
Combatting Adversarial Attacks through Denoising and Dimensionality Reduction: A Cascaded Autoencoder ApproachCode0
Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks—0
Time Series Featurization via Topological Data Analysis—0
SqueezeFit: Label-aware dimensionality reduction by semidefinite programmingCode0
Machine Learning of coarse-grained Molecular Dynamics Force Fields—0
Exploiting Wireless Channel State Information Structures Beyond Linear Correlations: A Deep Learning Approach—0
GAN-EM: GAN based EM learning framework—0
Robust Subspace Approximation in a Stream—0
Model-based targeted dimensionality reduction for neuronal population data—0
Manifold Coordinates with Physical MeaningCode0
RetinaMatch: Efficient Template Matching of Retina Images for Teleophthalmology—0
A Visual Interaction Framework for Dimensionality Reduction Based Data Exploration—0
Detailed Investigation of Deep Features with Sparse Representation and Dimensionality Reduction in CBIR: A Comparative Study—0
Enhanced Expressive Power and Fast Training of Neural Networks by Random ProjectionsCode0
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes—0
Global Sensitivity Analysis of High Dimensional Neuroscience Models: An Example of Neurovascular Coupling—0
A Semi-supervised Spatial Spectral Regularized Manifold Local Scaling Cut With HGF for Dimensionality Reduction of Hyperspectral Images—0
A case study : Influence of Dimension Reduction on regression trees-based Algorithms -Predicting Aeronautics Loads of a Derivative Aircraft—0
Exploring the Deep Feature Space of a Cell Classification Neural Network—0
Subspace Clustering through Sub-ClustersCode0
Unsupervised learning with contrastive latent variable modelsCode0
Interactive dimensionality reduction using similarity projections—0
Matrix Product Operator Restricted Boltzmann Machines—0
Estimation of Dimensions Contributing to Detected Anomalies with Variational Autoencoders—0
Semi-supervised Deep Representation Learning for Multi-View Problems—0
Exploiting Capacity of Sewer System Using Unsupervised Learning Algorithms Combined with Dimensionality Reduction—0
Performance of Johnson-Lindenstrauss Transform for k-Means and k-Medians Clustering—0
Nonlinear Dimension Reduction via Outer Bi-Lipschitz Extensions—0
GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training—0
Generative Adversarial Speaker Embedding Networks for Domain Robust End-to-End Speaker Verification—0
SRP: Efficient class-aware embedding learning for large-scale data via supervised random projectionsCode0
Representation Learning by Reconstructing Neighborhoods—0
Unsupervised representation learning using convolutional and stacked auto-encoders: a domain and cross-domain feature space analysis—0
The Price of Fair PCA: One Extra DimensionCode0
Dimensionality Reduction has Quantifiable Imperfections: Two Geometric Bounds—0
Unsupervised Dimension Selection using a Blue Noise Spectrum—0
Non-linear Canonical Correlation Analysis: A Compressed Representation Approach—0
Contrastive Multivariate Singular Spectrum Analysis—0
Alternating Diffusion Map Based Fusion of Multimodal Brain Connectivity Networks for IQ Prediction—0
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation—0
Scaling Gaussian Process Regression with DerivativesCode0
Failing Loudly: An Empirical Study of Methods for Detecting Dataset ShiftCode0
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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