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Found 11 papers, 4 papers with code
The Relevance Feature and Vector Machine for health applications
This paper presents the Relevance Feature and Vector Machine (RFVM), a novel model that addresses the challenges of the fat-data problem when dealing with clinical prospective studies.
Adaptive Sparse Gaussian Process
Next, to make the model inference as simple as possible, we propose updating a single inducing point of the sparse GP model together with the remaining model parameters every time a new sample arrives.
Bayesian learning of feature spaces for multitasks problems
This paper introduces a novel approach for multi-task regression that connects Kernel Machines (KMs) and Extreme Learning Machines (ELMs) through the exploitation of the Random Fourier Features (RFFs) approximation of the RBF kernel.
Multimodal hierarchical Variational AutoEncoders with Factor Analysis latent space
For this purpose, recent studies based on deep generative models merge all views into a nonlinear complex latent space, which can share information among views.
Multi-task longitudinal forecasting with missing values on Alzheimer's Disease
Machine learning techniques typically applied to dementia forecasting lack in their capabilities to jointly learn several tasks, handle time dependent heterogeneous data and missing values.
Nonnegative OPLS for Supervised Design of Filter Banks: Application to Image and Audio Feature Extraction
Audio or visual data analysis tasks usually have to deal with high-dimensional and nonnegative signals.
Regularized Multivariate Analysis Framework for Interpretable High-Dimensional Variable Selection
Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction which exploit correlations among input variables representing the data.
A conditional one-output likelihood formulation for multitask Gaussian processes
At the same time, by extending this approach with both a hierarchical and an approximate model, the proposed extensions are capable of recovering the multitask covariance and noise matrices after learning only $2T$ parameters, avoiding the validation of any model hyperparameter and reducing the overall complexity of the model as well as the risk of overfitting.
Bayesian Sparse Factor Analysis with Kernelized Observations
In particular, we combine probabilistic factor analysis with what we refer to as kernelized observations, in which the model focuses on reconstructing not the data itself, but its relationship with other data points measured by a kernel function.
Sparse Semi-supervised Heterogeneous Interbattery Bayesian Analysis
The Bayesian approach to feature extraction, known as factor analysis (FA), has been widely studied in machine learning to obtain a latent representation of the data.
Why (and How) Avoid Orthogonal Procrustes in Regularized Multivariate Analysis
Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction that exploit correlations among input variables of the data representation.
