Actually the growing volume of data provided by different sources some times may present inconsistencies, the data could be incomplete with lack of values or containing aggregate data, noisy containing errors or outliers, etc. Then data cleaning consist in filling missing values, smooth noisy data, identify or remove outliers and resolve inconsistencies. In more general definition, data cleanin...

In this article, we present a collection of fifteen novel contributions on machine learning methods with low-quality or imperfect datasets, which were accepted for publication in the special issue “Machine Learning Methods Noisy, Incomplete Small Datasets”, Applied Sciences (ISSN 2076-3417). These papers provide variety approaches to real-world problems where available datasets suffer from impe...

Operational modal analysis deals with the estimation of modal parameters using vibration data obtained from unknown or ambient excitation sources in operational rather than laboratory conditions. The paper compares three operational modal parameter identification approaches for identifying modal parameters of a multi-span concrete motorway bridge when subjected to either ambient or broad-band l...

In this paper we develop a neural network which learns intermodal representations for fashion attributes to be utilized in a cross-modal search tool. Our neural network learns from organic e-commerce data, which is characterized by clean image material, but noisy and incomplete product descriptions. First, we experiment with techniques to segment ecommerce images and their product descriptions ...

We present a procedure that can estimate – with statistical consistency guarantees – any local-maxima of a density, under benign distributional conditions. The procedure estimates all such local maxima, or modal-sets, of any bounded shape or dimension, including usual point-modes. In practice, modal-sets can arise as dense low-dimensional structures in noisy data, and more generally serve to be...

We propose a general and uniform modal framework for the Event Calculus (EC) and its skeptical and credolous variants. The resulting temporal formalism, called the Generalized Modal Event Calculus (GMEC), extends considerably the expressive power of EC when information about the ordering of events is incomplete. It provides means of inquiring about the evolution of the maximal validity interval...

Real-world network datasets often contain a wealth of complex topological information. In the face of these data, researchers often employ methods to extract reduced networks containing the most important structures or pathways, sometimes known as ‘skeletons’ or ‘backbones’. Numerous such methods have been developed. Yet data are often noisy or incomplete, with unknown numbers of missing or spu...