In this article, we discuss two novel sparse versions of the classical nearest-centroid classifier. The proposed sparse classifiers are based on ℓ₁ and ℓ₂ distance criteria, respectively, and perform simultaneous feature selection and classification, by detecting the features that are most relevant for the classification purpose. We formally prove that the training of the proposed sparse models, with both distance criteria, can be performed exactly (i.e., the globally optimal set of features is selected) at a linear computational cost. Especially, the proposed sparse classifiers are trained in O(mn)+O(młog k) operations, where n is the number of samples, m is the total number of features, and k łeq m is the number of features to be retained in the classifier. Furthermore, the complexity of testing and classifying a new sample is simply O(k) for both methods. The proposed models can be employed either as stand-alone sparse classifiers or fast feature-selection techniques for prefiltering the features to be later fed to other types of classifiers (e.g., SVMs). The experimental results show that the proposed methods are competitive in accuracy with state-of-the-art feature selection and classification techniques while having a substantially lower computational cost.
Sparse ℓ₁- and ℓ₂-Center Classifiers / Calafiore, G. C.; Fracastoro, G.. - In: IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS. - ISSN 2162-237X. - STAMPA. - 33:3(2022), pp. 996-1009. [10.1109/TNNLS.2020.3036838]
Sparse ℓ₁- and ℓ₂-Center Classifiers
Calafiore G. C.;Fracastoro G.
2022
Abstract
In this article, we discuss two novel sparse versions of the classical nearest-centroid classifier. The proposed sparse classifiers are based on ℓ₁ and ℓ₂ distance criteria, respectively, and perform simultaneous feature selection and classification, by detecting the features that are most relevant for the classification purpose. We formally prove that the training of the proposed sparse models, with both distance criteria, can be performed exactly (i.e., the globally optimal set of features is selected) at a linear computational cost. Especially, the proposed sparse classifiers are trained in O(mn)+O(młog k) operations, where n is the number of samples, m is the total number of features, and k łeq m is the number of features to be retained in the classifier. Furthermore, the complexity of testing and classifying a new sample is simply O(k) for both methods. The proposed models can be employed either as stand-alone sparse classifiers or fast feature-selection techniques for prefiltering the features to be later fed to other types of classifiers (e.g., SVMs). The experimental results show that the proposed methods are competitive in accuracy with state-of-the-art feature selection and classification techniques while having a substantially lower computational cost.File | Dimensione | Formato | |
---|---|---|---|
FINAL_VERSION.pdf
accesso aperto
Tipologia:
2. Post-print / Author's Accepted Manuscript
Licenza:
PUBBLICO - Tutti i diritti riservati
Dimensione
3.55 MB
Formato
Adobe PDF
|
3.55 MB | Adobe PDF | Visualizza/Apri |
Sparse_1-_and_2-Center_Classifiers.pdf
non disponibili
Tipologia:
2a Post-print versione editoriale / Version of Record
Licenza:
Non Pubblico - Accesso privato/ristretto
Dimensione
1.42 MB
Formato
Adobe PDF
|
1.42 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
Pubblicazioni consigliate
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
https://hdl.handle.net/11583/2957810