Deep transfer learning for classification of time-delayed Gaussian networks
Ong, Yew Soon
Arumugam, R. V.
Date of Issue2014
School of Computer Engineering
In this paper, we propose deep transfer learning for classifcation of Gaussian networks with time-delayed regulations. To ensure robust signaling, most real world problems from related domains have inherent alternate pathways that can be learned incrementally from a stable form of the baseline. In this paper, we leverage on this characteristic to address the challenges of complexity and scalability. The key idea is to learn high dimensional network motifs from low dimensional forms through a process of transfer learning. In contrast to previous work, we facilitate positive transfer by introducing a triangular inequality constraint, which provides a measure for the feasibility of mapping between di erent motif manifolds. Network motifs from different classes of Gaussian networks are used collectively to pre-train a deep neural network governed by a Lyapunov stability condition. The proposed framework is validated on time series data sampled from synthetic Gaussian networks and applied to a real world dataset for the classi cation of basketball games based on skill level. We observe an improvement in the range of [15-25]% in accuracy and a saving in the range of [25-600]% in computational cost on synthetic as well as realistic networks with time-delays when compared to existing state-of-the-art approaches. In addition, new insights into meaningful o ensive formations in the Basketball games can be derived from the deep network.
Deep Neural Networks
Deep Neural Networks
© 2014 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Signal Processing, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [http://dx.doi.org/10.1016/j.sigpro.2014.09.009].