This is the abstract of our recent paper at IJCNN 2017. This method addresses the problem of time series classification on low-dimensional and heterogeneous feature space with the use of recurrent neural networks.

**Abstract**

We present Partition-wise Gated Recurrent Units (pGRUs) for point-based trajectory classification to detect real-world trawler fishing activities in the ocean. We propose partition-wise activation functions integrated with Gated Recurrent Units that partitions each feature and uses independent parameters to model distinct regions of the feature space. This approach enables us to leverage the benefits of deep learning on a low-dimensional and heterogeneous feature space by mapping the low-dimensional features into another space that can be better separated with piece-wise learnable parameters. We show that the proposed partition-wise activation functions can approximate a wide array of functions, including conventional activations such as sigmoid and hyperbolic tangent. Our experimental results demonstrate that pGRU learns the probability distribution from data and achieves substantial improvements over the state-of-the-art systems on the task of trawler fishing activity detection.

**Slides**

The slides are available at here.