We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset. To this end, we introduce a Convolutional Cluster Pooling layer exploiting a multi-scale clustering in order to highlight, at different resolutions, locally connected regions on the input graph. Our proposal generalises well-established neural models such as Convolutional Neural Networks (CNNs) on irregular and complex domains, by means of the exploitation of the weight sharing property in a graph-oriented architecture. In this work, such property is based on the centrality of each vertex within its soft-assigned cluster. Extensive experiments on NTU RGB+D, CIFAR-10 and 20NEWS demonstrate the effectiveness of the proposed technique in capturing both local and global patterns in graph-structured data out of different domains.

Classifying Signals on Irregular Domains via Convolutional Cluster Pooling / Porrello, Angelo; Abati, Davide; Calderara, Simone; Cucchiara, Rita. - (2019). ((Intervento presentato al convegno AISTATS 2019 - The 22nd International Conference on Artificial Intelligence and Statistics tenutosi a Naha, Okinawa, Japan nel Tuesday, 16 April 2019.

Classifying Signals on Irregular Domains via Convolutional Cluster Pooling

Angelo Porrello;Davide Abati;Simone Calderara;Rita Cucchiara
2019

Abstract

We present a novel and hierarchical approach for supervised classification of signals spanning over a fixed graph, reflecting shared properties of the dataset. To this end, we introduce a Convolutional Cluster Pooling layer exploiting a multi-scale clustering in order to highlight, at different resolutions, locally connected regions on the input graph. Our proposal generalises well-established neural models such as Convolutional Neural Networks (CNNs) on irregular and complex domains, by means of the exploitation of the weight sharing property in a graph-oriented architecture. In this work, such property is based on the centrality of each vertex within its soft-assigned cluster. Extensive experiments on NTU RGB+D, CIFAR-10 and 20NEWS demonstrate the effectiveness of the proposed technique in capturing both local and global patterns in graph-structured data out of different domains.
2019
AISTATS 2019 - The 22nd International Conference on Artificial Intelligence and Statistics
Naha, Okinawa, Japan
Tuesday, 16 April 2019
Porrello, Angelo; Abati, Davide; Calderara, Simone; Cucchiara, Rita
Classifying Signals on Irregular Domains via Convolutional Cluster Pooling / Porrello, Angelo; Abati, Davide; Calderara, Simone; Cucchiara, Rita. - (2019). ((Intervento presentato al convegno AISTATS 2019 - The 22nd International Conference on Artificial Intelligence and Statistics tenutosi a Naha, Okinawa, Japan nel Tuesday, 16 April 2019.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11380/1171251
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