Abstract
Three important issues are often encountered in Supervised Classifica-
tion: class-memberships are unreliable for some training units (Label Noise), a pro-
portion of observations might depart from the bulk of the data structure (Outliers) and
groups represented in the test set may have not been encountered earlier in the learn-
ing phase (Unobserved Classes). The present work introduces a Robust and Adaptive
Eigenvalue-Decomposition Discriminant Analysis (RAEDDA) capable of handling
situations in which one or more of the afore described problems occur. Transductive
and inductive robust EM-based procedures are proposed for parameter estimation and
experiments on real data, artificially adulterated, are provided to underline the benefits
of the proposed method.
Lingua originale | English |
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Titolo della pubblicazione ospite | Cladag2019 : Book of short papers |
Pagine | 104-107 |
Numero di pagine | 4 |
Stato di pubblicazione | Pubblicato - 2019 |
Evento | Scientific Meeting Classification and Data Analysis Group - Cassino Durata: 11 set 2019 → 13 set 2019 |
Convegno
Convegno | Scientific Meeting Classification and Data Analysis Group |
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Città | Cassino |
Periodo | 11/9/19 → 13/9/19 |
Keywords
- model-based classification
- unobserved classes
- robust estimation
- outliers detection
- impartial trimming
- label noise