SOTAVerified

Dimensionality Reduction

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

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Papers

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Showing 2451–2500 of 3304 papers

TitleStatusHype
Learning Optimal Deep Projection of ^18F-FDG PET Imaging for Early Differential Diagnosis of Parkinsonian Syndromes—0
Real time expert system for anomaly detection of aerators based on computer vision technology and existing surveillance cameras—0
Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations—0
Hartley Spectral Pooling for Deep LearningCode1
Network Distance Based on Laplacian Flows on Graphs—0
Computer vision-based framework for extracting geological lineaments from optical remote sensing dataCode0
CoverBLIP: accelerated and scalable iterative matched-filtering for Magnetic Resonance Fingerprint reconstructionCode0
Capturing Regional Variation with Distributed Place Representations and Geographic Retrofitting—0
Quantifying Context Overlap for Training Word Embeddings—0
Dynamic Sparse Graph for Efficient Deep Learning—0
Modeling Dynamics of Biological Systems with Deep Generative Neural Networks—0
Graph Laplacian Regularized Graph Convolutional Networks for Semi-supervised LearningCode0
Graph filtering for data reduction and reconstruction—0
A convex formulation for high-dimensional sparse sliced inverse regression—0
ManifoldNet: A Deep Network Framework for Manifold-valued DataCode1
Visualization of High-dimensional Scalar Functions Using Principal ParameterizationsCode0
Distance preserving model order reduction of graph-Laplacians and cluster analysis—0
Randomized Iterative Algorithms for Fisher Discriminant Analysis—0
Mixtures of Skewed Matrix Variate Bilinear Factor AnalyzersCode0
Optimal Sparse Singular Value Decomposition for High-dimensional High-order Data—0
A note on concentration inequality for vector-valued martingales with weak exponential-type tails—0
Geometry of Deep Learning for Magnetic Resonance Fingerprinting—0
NEU: A Meta-Algorithm for Universal UAP-Invariant Feature Representation—0
Open Source Dataset and Machine Learning Techniques for Automatic Recognition of Historical Graffiti—0
A novel extension of Generalized Low-Rank Approximation of Matrices based on multiple-pairs of transformations—0
Scalable Manifold Learning for Big Data with Apache SparkCode0
A DEEP ADVERSARIAL LEARNING METHODOLOGY FOR DESIGNING MICROSTRUCTURAL MATERIAL SYSTEMSCode0
Parameter-wise co-clustering for high-dimensional data—0
Vehicles Lane-changing Behavior Detection—0
XPCA: Extending PCA for a Combination of Discrete and Continuous Variables—0
Supervised Kernel PCA For Longitudinal Data—0
Fourier analysis perspective for sufficient dimension reduction problem—0
A Projector-Based Approach to Quantifying Total and Excess Uncertainties for Sketched Linear Regression—0
DNN Feature Map Compression using Learned Representation over GF(2)Code0
Efficient Outlier Removal in Large Scale Global Structure-from-MotionCode0
Low-complexity 8-point DCT Approximation Based on Angle Similarity for Image and Video Coding—0
Feature Dimensionality Reduction for Video Affect Classification: A Comparative Study—0
Efficient Principal Subspace Projection of Streaming Data Through Fast Similarity Matching—0
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets—0
Hybrid Subspace Learning for High-Dimensional Data—0
Model-Free Context-Aware Word Composition—0
Neural Activation Semantic Models: Computational lexical semantic models of localized neural activationsCode0
TRAPACC and TRAPACCS at PARSEME Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions—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
Multi-view Reconstructive Preserving Embedding for Dimension Reduction—0
Prototype Discovery using Quality-Diversity—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