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 2501–2550 of 3304 papers

TitleStatusHype
A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation—0
A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering—0
A clustering adaptive Gaussian process regression method: response patterns based real-time prediction for nonlinear solid mechanics problems—0
A cognitive based Intrusion detection system—0
A bi-partite generative model framework for analyzing and simulating large scale multiple discrete-continuous travel behaviour data—0
A Compact and Discriminative Face Track Descriptor—0
A Comparative Analysis of Principal Component Analysis (PCA) and Singular Value Decomposition (SVD) as Dimensionality Reduction Techniques—0
A Comparative Study of Text Preprocessing Approaches for Topic Detection of User Utterances—0
A comparison of latent semantic analysis and correspondence analysis of document-term matrices—0
A Comparison of Neural Network Training Methods for Text Classification—0
A Comparison of Representation Learning Methods for Dimensionality Reduction of fMRI Scans for Classification of ADHD—0
A Comparison Study on Nonlinear Dimension Reduction Methods with Kernel Variations: Visualization, Optimization and Classification—0
A Comprehensive Filter Feature Selection for Improving Document Classification—0
A Computationally Efficient Method for Defending Adversarial Deep Learning Attacks—0
A Uniform Concentration Inequality for Kernel-Based Two-Sample Statistics—0
A Confident Information First Principle for Parametric Reduction and Model Selection of Boltzmann Machines—0
A contextual analysis of multi-layer perceptron models in classifying hand-written digits and letters: limited resources—0
A convex formulation for high-dimensional sparse sliced inverse regression—0
A Convex formulation for linear discriminant analysis—0
A Convex Formulation for Spectral Shrunk Clustering—0
A Convex Sparse PCA for Feature Analysis—0
A Correspondence Analysis Framework for Author-Conference Recommendations—0
Multivariate Analysis for Multiple Network Data via Semi-Symmetric Tensor PCA—0
A Critical Note on the Evaluation of Clustering Algorithms—0
A Cross Entropy test allows quantitative statistical comparison of t-SNE and UMAP representations—0
Active Learning with TensorBoard Projector—0
Active Linear Regression for _p Norms and Beyond—0
A Curated Image Parameter Dataset from Solar Dynamics Observatory Mission—0
Adapting Speaker Embeddings for Speaker Diarisation—0
Adaptive Dimension Reduction and Variational Inference for Transductive Few-Shot Classification—0
Adaptive Down-Sampling and Dimension Reduction in Time Elastic Kernel Machines for Efficient Recognition of Isolated Gestures—0
Adaptive Locally Linear Embedding—0
Adaptive Metric Dimensionality Reduction—0
Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning—0
Adaptive physics-informed neural operator for coarse-grained non-equilibrium flows—0
Adaptive Randomized Dimension Reduction on Massive Data—0
A Dashboard to Analysis and Synthesis of Dimensionality Reduction Methods in Remote Sensing—0
A Data-Driven and Distributed Approach to Sparse Signal Representation and Recovery—0
A data-driven approach for multiscale elliptic PDEs with random coefficients based on intrinsic dimension reduction—0
A Data Quarantine Model to Secure Data in Edge Computing—0
A Dataset Similarity Evaluation Framework for Wireless Communications and Sensing—0
Additive Component Analysis—0
A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe—0
A Deep Embedded Refined Clustering Approach for Breast Cancer Distinction based on DNA Methylation—0
A Deep Graph Embedding Network Model for Face Recognition—0
A Deep Learning approach for parametrized and time dependent Partial Differential Equations using Dimensionality Reduction and Neural ODEs—0
A Deep Learning Approach with an Attention Mechanism for Automatic Sleep Stage Classification—0
A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017—0
A Deep Signed Directional Distance Function for Object Shape Representation—0
A-DenseUNet: Adaptive Densely Connected UNet for Polyp Segmentation in Colonoscopy Images with Atrous Convolution—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