Algorithm analysis of MCU automatic trimming

Contamination can severely distort an estimator unless the estimation procedure is suitably robust. This is a well-known issue and has been addressed in Robust Statistics, however, the relation of contamination and distorted variable selection has ...

Clustering is one of the most widely used unsupervised learning techniques. However, it is well‐known that outliers can have a significantly adverse impact on commonly applied clustering methods. On the other hand, clustered outliers can be ...

Impartial trimming in Cluster Analysis allows for the exclusion of a fraction of potentially outlying observations (depicted by crosses in this graph). This approach results in robust clustering methods that can withstand anomalous or noisy data, while ...

The operation of trimming data sets is heavily used in AI systems. Trimming is useful to make AI systems more robust against adversarial or common perturbations. At the core of robust AI systems lies the concept that outliers in a data set occur with ...