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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 1401–1450 of 3304 papers

TitleStatusHype
Computational Graph Completion—0
Sufficient Dimension Reduction for High-Dimensional Regression and Low-Dimensional Embedding: Tutorial and Survey—0
A Dimensionality Reduction Approach for Convolutional Neural Networks—0
Dimensionality Reduction for Wasserstein Barycenter—0
TLDR: Twin Learning for Dimensionality ReductionCode1
Persuasion by Dimension Reduction—0
Compression-aware Projection with Greedy Dimension Reduction for Convolutional Neural Network Activations—0
Data-driven intrinsic localized mode detection and classification in one-dimensional crystal lattice model—0
SGEN: Single-cell Sequencing Graph Self-supervised Embedding Network—0
Unsupervised Data-Driven Nuclei Segmentation For Histology Images—0
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance—0
End-to-end translation of human neural activity to speech with a dual-dual generative adversarial network—0
Incremental Ensemble Gaussian Processes—0
Label scarcity in biomedicine: Data-rich latent factor discovery enhances phenotype prediction—0
Nonnegative spatial factorizationCode1
Domain Generalization via Domain-based Covariance Minimization—0
Automatic Recognition of Abdominal Organs in Ultrasound Images based on Deep Neural Networks and K-Nearest-Neighbor ClassificationCode1
EnsembleNTLDetect: An Intelligent Framework for Electricity Theft Detection in Smart Grid—0
Cognitive Coding of Speech—0
Advancing the dimensionality reduction of speaker embeddings for speaker diarisation: disentangling noise and informing speech activity—0
AgFlow: Fast Model Selection of Penalized PCA via Implicit Regularization Effects of Gradient Flow—0
T-SNE Is Not Optimized to Reveal Clusters in Data—0
Efficient GPU implementation of randomized SVD and its applications—0
On the Correspondence between Gaussian Processes and Geometric Harmonics—0
Classification of high-dimensional data with spiked covariance matrix structure—0
Robust Linear Classification from Limited Training Data—0
Pharmacoprint -- a combination of pharmacophore fingerprint and artificial intelligence as a tool for computer-aided drug design—0
DenDrift: A Drift-Aware Algorithm for Host Profiling—0
Dimension Reduction for Fréchet Regression—0
Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM)Code1
Weight Vector Tuning and Asymptotic Analysis of Binary Linear Classifiers—0
Visual Cluster Separation Using High-Dimensional Sharpened Dimensionality Reduction—0
Learning Universal User Representations via Self-Supervised Lifelong Behaviors Modeling—0
Learning Stochastic Representations of Physical Systems—0
An Efficient and Reliable Tolerance-Based Algorithm for Principal Component Analysis—0
SpaceMAP: Visualizing Any Data in 2-dimension by Space Expansion—0
Less is More: Dimension Reduction Finds On-Manifold Adversarial Examples in Hard-Label Attacks—0
Cell2State: Learning Cell State Representations From Barcoded Single-Cell Gene-Expression Transitions—0
A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction—0
Long-time prediction of nonlinear parametrized dynamical systems by deep learning-based ROMs—0
Dimension Reduction for Data with Heterogeneous MissingnessCode0
Non-Euclidean Self-Organizing Maps—0
IRMAC: Interpretable Refined Motifs in Binary Classification for Smart Grid Applications—0
The Curse Revisited: When are Distances Informative for the Ground Truth in Noisy High-Dimensional Data?Code0
Weighted Low Rank Matrix Approximation and Acceleration—0
Data Augmentation Through Monte Carlo Arithmetic Leads to More Generalizable Classification in ConnectomicsCode0
Probabilistic Bearing Fault Diagnosis Using Gaussian Process with Tailored Feature Extraction—0
Machine-Learned HASDM Model with Uncertainty Quantification—0
A Comparative Study of Machine Learning Methods for Predicting the Evolution of Brain Connectivity from a Baseline TimepointCode0
Disentangling Generative Factors of Physical Fields Using Variational Autoencoders—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