Generative Kernels for Tree-Structured Data
By Davide Bacciu, Alessio Micheli, Alessandro Sperduti. 2018
24 citations recorded by the source corpus.
This paper presents a family of methods for the design of adaptive kernels for tree-structured data that exploits the summarization properties of hidden states of hidden Markov models for trees. We introduce a compact and discriminative feature space based on the concept of hidden states multisets and we discuss different approaches to estimate such hidden state encoding. We show how it can be used to build an efficient and general tree kernel based on Jaccard similarity. Furthermore, we derive an unsupervised convolutional generative kernel using a topology induced on the Markov states by a tree topographic mapping. This paper provides an extensive empirical assessment on a variety of structured data learning tasks, comparing the predictive accuracy and computational efficiency of state-of-the-art generative, adaptive, and syntactical tree kernels. The results show that the proposed generative approach has a good tradeoff between computational complexity and predictive performance, in particular when considering the soft matching introduced by the topographic mapping.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Alessandro Sperduti
- Nicoló Navarin
- Dinh Van Tran
- Giovanni Da San Martino
- Christopher Morris
- Nils M. Kriege
- Fredrik D. Johansson
In Computer Vision and Pattern Recognition
In Signal Processing
Citations in the corpus
- Pre-training Graph Neural Networks with Kernels (2018, neutral): “…there are examples of kernels for structures that can be designed on the basis of a training set [van der Maaten, 2011; Aiolli et al. , 2009; Bacciu et al. , 2018], most of the more efficient and effective graph kernels are based on predefined structural features, i.e, features definition is…”
- On Filter Size in Graph Convolutional Networks (2018, background): “Although there are examples of kernels for structures that can be designed on the basis of a training set [4]–[6], most of the more efficient and effective graph kernels are based on predefined structural features, i.”