activity
20172024
most citedNoisy multi-label semi-supervised dimensionality reduction

41 citations · 44 across the 6 of their papers we have counts for

collaborators

8 papers

cs.CV2024★ 3 cited

LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks

Vanesa Gómez-Martínez, Francisco J. Lara-Abelenda, Pablo Peiro-Corbacho +3

Tabular data have been extensively used in different knowledge domains. Convolutional neural networks (CNNs) have been successfully used in many applications where important inform…

cs.LG2021

On the Use of Time Series Kernel and Dimensionality Reduction to Identify the Acquisition of Antimicrobial Multidrug Resistance in the Intensive Care Unit

Óscar Escudero-Arnanz, Joaquín Rodríguez-Álvarez, Karl Øyvind Mikalsen +2

The acquisition of Antimicrobial Multidrug Resistance (AMR) in patients admitted to the Intensive Care Units (ICU) is a major global concern. This study analyses data in the form o…

stat.ML2020

A Kernel to Exploit Informative Missingness in Multivariate Time Series from EHRs

Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Robert Jenssen

A large fraction of the electronic health records (EHRs) consists of clinical measurements collected over time, such as lab tests and vital signs, which provide important informati…

cs.LG2019

Time series cluster kernels to exploit informative missingness and incomplete label information

Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi +2

The time series cluster kernel (TCK) provides a powerful tool for analysing multivariate time series subject to missing data. TCK is designed using an ensemble learning approach in…

stat.ML2019★ 41 cited

Noisy multi-label semi-supervised dimensionality reduction

Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi +1

Noisy labeled data represent a rich source of information that often are easily accessible and cheap to obtain, but label noise might also have many negative consequences if not ac…

stat.ML2018

An Unsupervised Multivariate Time Series Kernel Approach for Identifying Patients with Surgical Site Infection from Blood Samples

Karl Øyvind Mikalsen, Cristina Soguero-Ruiz, Filippo Maria Bianchi +2

A large fraction of the electronic health records consists of clinical measurements collected over time, such as blood tests, which provide important information about the health s…