Projection pursuit is a multivariate statistical technique aimed at finding interesting low-
dimensional data projections by maximizing a measure of interestingness commonly known …

There exist multiple methods to detect outliers in multivariate data in the literature, but most
of them require to estimate the covariance matrix. The higher the dimension, the more …

The prediction of the ultimate bearing capacity (UBC) of composite foundations represents a
critical application of test monitoring data within the field of intelligent geotechnical …

For any outlier detection procedure, a key concern is robustness with respect to possible
misclassification errors, masking (Type I) and swamping (Type II). Although parametric …

Invariant coordinate selection (ICS) is an unsupervised multivariate data transformation
useful in many contexts such as outlier detection or clustering. It is based on the …

In exploratory data analysis and data mining in the very common setting of a data set X of
vectors from Rd, the search for important features and artifacts of a geometrical nature is a …

La détection d'observations atypiques ou d'anomalies est un challenge dans de nombreux
domaines. Dans cet article, une revue de la littérature des méthodes non-supervisées est …

Random projections project high-dimensional data into a lower dimensional subspace that
has been randomly chosen. They are used in problems that require handling reduced …

La détection d'observations atypiques de manière non-supervisée est un enjeu crucial dans
la pratique de la statistique. Dans le domaine de la détection de défauts industriels, cette …

From the past decade outlier detection has been in use. Detection of outliers is an emerging
topic and is having robust applications in medical sciences and pharmaceutical sciences …