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Transfer Learning with Kernel Methods [PDF]

Nature Communications, 2023
Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple models that are competitive on a variety of tasks, it has been unclear how to develop ...
Adityanarayanan Radhakrishnan, Max Ruiz Luyten, Neha Prasad, Caroline Uhler   +3 more
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Comparative Analysis of Support Vector Machine Variants for Human Activity Recognition Using Wearable Sensor Data [PDF]

Sensors
Human Activity Recognition (HAR) using wearable sensor data is widely applied in healthcare, smart environments, and mobile systems. Support Vector Machines (SVMs) are commonly used for HAR due to their strong generalization ability. However, traditional
Minh Long Hoang
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Kernel-Based Independence Tests for Causal Structure Learning on Functional Data

Entropy, 2023
Measurements of systems taken along a continuous functional dimension, such as time or space, are ubiquitous in many fields, from the physical and biological sciences to economics and engineering.
Felix Laumann, Julius von Kügelgen, Junhyung Park, Bernhard Schölkopf, Mauricio Barahona   +4 more
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Kernel Geometric Mean Metric Learning

Applied Sciences, 2023
Geometric mean metric learning (GMML) algorithm is a novel metric learning approach proposed recently. It has many advantages such as unconstrained convex objective function, closed form solution, faster computational speed, and interpretability over ...
Zixin Feng, Teligeng Yun, Yu Zhou, Ruirui Zheng, Jianjun He   +4 more
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A Big Data Approach to Customer Relationship Management Strategy in Hospitality Using Multiple Correspondence Domain Description

Applied Sciences, 2020
COVID-19 has hit the hotel sector in a hitherto unknown way. This situation is producing a fundamental change in client behavior that makes crucial an adequate knowledge of their profile to overcome an uncertain environment.
Lydia González-Serrano, Pilar Talón-Ballestero, Sergio Muñoz-Romero, Cristina Soguero-Ruiz, José Luis Rojo-Álvarez   +4 more
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Data-Driven Supervised Learning for Life Science Data

Frontiers in Applied Mathematics and Statistics, 2020
Life science data are often encoded in a non-standard way by means of alpha-numeric sequences, graph representations, numerical vectors of variable length, or other formats. Domain-specific or data-driven similarity measures like alignment functions have
Maximilian Münch, Maximilian Münch, Christoph Raab, Christoph Raab, Michael Biehl, Frank-Michael Schleif, Frank-Michael Schleif, Frank-Michael Schleif   +7 more
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Efficient Kernel Cook's Distance for Remote Sensing Anomalous Change Detection

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020
Detecting anomalous changes in remote sensing images is a challenging problem, where many approaches and techniques have been presented so far. We rely on the standard field of multivariate statistics of diagnostic measures, which are concerned about the
Jose Antonio Padron-Hidalgo, Adrian Perez-Suay, Fatih Nar, Valero Laparra, Gustau Camps-Valls   +4 more
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TreeKernel: interpretable kernel machine tests for interactions between -omics and clinical predictors with applications to metabolomics and COPD phenotypes

BMC Bioinformatics, 2023
Background In this paper, we are interested in interactions between a high-dimensional -omics dataset and clinical covariates. The goal is to evaluate the relationship between a phenotype of interest and a high-dimensional omics pathway, where the effect
Charlie M. Carpenter, Lucas Gillenwater, Russell Bowler, Katerina Kechris, Debashis Ghosh   +4 more
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Sparse Sliding-Window Kernel Recursive Least-Squares Channel Prediction for Fast Time-Varying MIMO Systems

Sensors, 2022
Accurate channel state information (CSI) is important for MIMO systems, especially in a high-speed scenario, fast time-varying CSI tends to be out of date, and a change in CSI shows complex nonlinearities.
Xingxing Ai, Jiayi Zhao, Hongtao Zhang, Yong Sun   +3 more
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DEMANDE: Density Matrix Neural Density Estimation

IEEE Access, 2023
Density estimation is a fundamental task in statistics and machine learning that aims to estimate, from a set of samples, the probability density function of the distribution that generated them.
Joseph A. Gallego-Mejia, Fabio A. Gonzalez   +1 more
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