2,342 research outputs found
Transfer Learning across Networks for Collective Classification
This paper addresses the problem of transferring useful knowledge from a
source network to predict node labels in a newly formed target network. While
existing transfer learning research has primarily focused on vector-based data,
in which the instances are assumed to be independent and identically
distributed, how to effectively transfer knowledge across different information
networks has not been well studied, mainly because networks may have their
distinct node features and link relationships between nodes. In this paper, we
propose a new transfer learning algorithm that attempts to transfer common
latent structure features across the source and target networks. The proposed
algorithm discovers these latent features by constructing label propagation
matrices in the source and target networks, and mapping them into a shared
latent feature space. The latent features capture common structure patterns
shared by two networks, and serve as domain-independent features to be
transferred between networks. Together with domain-dependent node features, we
thereafter propose an iterative classification algorithm that leverages label
correlations to predict node labels in the target network. Experiments on
real-world networks demonstrate that our proposed algorithm can successfully
achieve knowledge transfer between networks to help improve the accuracy of
classifying nodes in the target network.Comment: Published in the proceedings of IEEE ICDM 201
Adaptive Feature Ranking for Unsupervised Transfer Learning
Transfer Learning is concerned with the application of knowledge gained from solving a problem to a different but related problem domain. In this paper, we propose a method and efficient algorithm for ranking and selecting representations from a Restricted Boltzmann Machine trained on a source domain to be transferred onto a target domain. Experiments carried out using the MNIST, ICDAR and TiCC image datasets show that the proposed adaptive feature ranking and transfer learning method offers statistically significant improvements on the training of RBMs. Our method is general in that the knowledge chosen by the ranking function does not depend on its relation to any specific target domain, and it works with unsupervised learning and knowledge-based transfer
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
Mining discriminative subgraph patterns from graph data has attracted great
interest in recent years. It has a wide variety of applications in disease
diagnosis, neuroimaging, etc. Most research on subgraph mining focuses on the
graph representation alone. However, in many real-world applications, the side
information is available along with the graph data. For example, for
neurological disorder identification, in addition to the brain networks derived
from neuroimaging data, hundreds of clinical, immunologic, serologic and
cognitive measures may also be documented for each subject. These measures
compose multiple side views encoding a tremendous amount of supplemental
information for diagnostic purposes, yet are often ignored. In this paper, we
study the problem of discriminative subgraph selection using multiple side
views and propose a novel solution to find an optimal set of subgraph features
for graph classification by exploring a plurality of side views. We derive a
feature evaluation criterion, named gSide, to estimate the usefulness of
subgraph patterns based upon side views. Then we develop a branch-and-bound
algorithm, called gMSV, to efficiently search for optimal subgraph features by
integrating the subgraph mining process and the procedure of discriminative
feature selection. Empirical studies on graph classification tasks for
neurological disorders using brain networks demonstrate that subgraph patterns
selected by the multi-side-view guided subgraph selection approach can
effectively boost graph classification performances and are relevant to disease
diagnosis.Comment: in Proceedings of IEEE International Conference on Data Mining (ICDM)
201
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