Experiments show that this is a proper methodology to highlight certain paragraphs in structured documents at the same time we learn interesting and more diverse topics.
Multivariate Analysis (MVA) comprises a family of well-known methods for feature extraction that exploit correlations among input variables of the data representation.
In this paper, we deal with distributed estimation problems in diffusion networks with heterogeneous nodes, i. e., nodes that either implement different adaptive rules or differ in some other aspect such as the filter structure or length, or step size.
In-network distributed estimation of sparse parameter vectors via diffusion LMS strategies has been studied and investigated in recent years.
Feature extraction and dimensionality reduction are important tasks in many fields of science dealing with signal processing and analysis.