Dimensionality Reduction

711 papers with code • 0 benchmarks • 10 datasets

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

( Image credit: openTSNE )

Libraries

Use these libraries to find Dimensionality Reduction models and implementations

Latest papers with no code

Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons

no code yet • 27 Mar 2024

Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms.

Representatividad Muestral en la Incertidumbre Simétrica Multivariada para la Selección de Atributos

no code yet • 27 Mar 2024

In this work, we analyze the behavior of the multivariate symmetric uncertainty (MSU) measure through the use of statistical simulation techniques under various mixes of informative and non-informative randomly generated features.

FedAC: A Adaptive Clustered Federated Learning Framework for Heterogeneous Data

no code yet • 25 Mar 2024

Clustered federated learning (CFL) is proposed to mitigate the performance deterioration stemming from data heterogeneity in federated learning (FL) by grouping similar clients for cluster-wise model training.

Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems

no code yet • 23 Mar 2024

In remedy, we use dimensionality-reduction techniques to measure the overlap between the target eye images and synthetic training data, and to prune the training dataset in a manner that maximizes distribution overlap.

Boarding for ISS: Imbalanced Self-Supervised: Discovery of a Scaled Autoencoder for Mixed Tabular Datasets

no code yet • 23 Mar 2024

This paper aims to fill this gap by examining the specific challenges posed by data imbalance in self-supervised learning in the domain of tabular data, with a primary focus on autoencoders.

Text clustering with LLM embeddings

no code yet • 22 Mar 2024

Text clustering is an important approach for organising the growing amount of digital content, helping to structure and find hidden patterns in uncategorised data.

A Wasserstein perspective of Vanilla GANs

no code yet • 22 Mar 2024

The assumptions of this oracle inequality are designed to be satisfied by network architectures commonly used in practice, such as feedforward ReLU networks.

Infrastructure-Assisted Collaborative Perception in Automated Valet Parking: A Safety Perspective

no code yet • 22 Mar 2024

Environmental perception in Automated Valet Parking (AVP) has been a challenging task due to severe occlusions in parking garages.

Multiple-Input Auto-Encoder Guided Feature Selection for IoT Intrusion Detection Systems

no code yet • 22 Mar 2024

The MIAE model is trained in an unsupervised learning mode to transform the heterogeneous inputs into lower-dimensional representation, which helps classifiers distinguish between normal behaviour and different types of attacks.

Frequency-dependent covariance reveals critical spatio-temporal patterns of synchronized activity in the human brain

no code yet • 22 Mar 2024

Recent analyses combining advanced theoretical techniques and high-quality data from thousands of simultaneously recorded neurons provide strong support for the hypothesis that neural dynamics operate near the edge of instability across regions in the brain.