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 2251–2300 of 3304 papers

TitleStatusHype
Time Series Featurization via Topological Data Analysis—0
Time series model selection with a meta-learning approach; evidence from a pool of forecasting algorithms—0
TIPAA-SSL: Text Independent Phone-to-Audio Alignment based on Self-Supervised Learning and Knowledge Transfer—0
tLaSDI: Thermodynamics-informed latent space dynamics identification—0
TL-PCA: Transfer Learning of Principal Component Analysis—0
TMI: Thermodynamic inference of data manifolds—0
Too many secants: a hierarchical approach to secant-based dimensionality reduction on large data sets—0
Topic Identification and Discovery on Text and Speech—0
Topological Data Analysis of Spatial Patterning in Heterogeneous Cell Populations: Clustering and Sorting with Varying Cell-Cell Adhesion—0
Topological Indoor Mapping through WiFi Signals—0
Topological Stability: a New Algorithm for Selecting The Nearest Neighbors in Non-Linear Dimensionality Reduction Techniques—0
Topology Analysis of International Networks Based on Debates in the United Nations—0
Topology of cognitive maps—0
TopoMap++: A faster and more space efficient technique to compute projections with topological guarantees—0
Toward a unified theory of sparse dimensionality reduction in Euclidean space—0
A Diffusion Approximation Theory of Momentum SGD in Nonconvex Optimization—0
Toward Guaranteed Illumination Models for Non-Convex Objects—0
Towards a Dynamic Future with Adaptable Computing and Network Convergence (ACNC)—0
Towards a Fairer Non-negative Matrix Factorization—0
Towards a machine learning pipeline in reduced order modelling for inverse problems: neural networks for boundary parametrization, dimensionality reduction and solution manifold approximation—0
Towards a Theoretical Analysis of PCA for Heteroscedastic Data—0
Towards Basque Oral Poetry Analysis: A Machine Learning Approach—0
Toward Scalable Machine Learning and Data Mining: the Bioinformatics Case—0
Towards Explainable Fusion and Balanced Learning in Multimodal Sentiment Analysis—0
Towards glass-box CNNs—0
Style Transfer with Time Series: Generating Synthetic Financial Data—0
Towards Making High Dimensional Distance Metric Learning Practical—0
Towards One Model for Classical Dimensionality Reduction: A Probabilistic Perspective on UMAP and t-SNE—0
Towards Robust Cross-Domain Domain Adaptation for Part-of-Speech Tagging—0
Trace Quotient Meets Sparsity: A Method for Learning Low Dimensional Image Representations—0
Trace Quotient with Sparsity Priors for Learning Low Dimensional Image Representations—0
Trace transform based method for color image domain identification—0
Trainable Compound Activation Functions for Machine Learning—0
Training Artificial Neural Networks by Coordinate Search Algorithm—0
Training Echo State Networks with Regularization through Dimensionality Reduction—0
Training (Overparametrized) Neural Networks in Near-Linear Time—0
Training-Time Attacks against k-Nearest Neighbors—0
Transcription Factor-DNA Binding Via Machine Learning Ensembles—0
Transfer Joint Matching for Unsupervised Domain Adaptation—0
Transfer learning-assisted inverse modeling in nanophotonics based on mixture density networks—0
Transfer Learning for Segmenting Dimensionally-Reduced Hyperspectral Images—0
Transfer Representation Learning with TSK Fuzzy System—0
Unsupervised Transfer Learning for Anomaly Detection: Application to Complementary Operating Condition Transfer—0
Transformation Based Deep Anomaly Detection in Astronomical Images—0
Transformer-based dimensionality reduction—0
Transform Invariant Auto-encoder—0
Transform Quantization for CNN (Convolutional Neural Network) Compression—0
TransG : A Generative Model for Knowledge Graph Embedding—0
TRAPACC and TRAPACCS at PARSEME Shared Task 2018: Neural Transition Tagging of Verbal Multiword Expressions—0
Traversing the Subspace of Adversarial Patches—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