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Manifold landmarks can approximately represent the low-dimensional nonlinear manifold structure embedded in high-dimensional ambient feature space. Due to the quadratic complexity of many learning algorithms in the number of training samples, selecting a sample subset as manifold landmarks has become an important issue for scalable learning. Unfort...
There has been an explosion in the usage of trajectory data. Clustering is one of the simplest and most powerful approaches for knowledge discovery from trajectories. In order to produce meaningful clusters, well-defined metrics are required to capture the essence of similarity between trajectories. One such distance function is Dynamic Time Warpin...
Many machine learning and pattern recognition algorithms rely heavily on good distance metrics to achieve competitive performance. While distance metrics can be learned, the computational expense of doing so is currently infeasible on large datasets. In this paper, we propose two efficient-and-effective approaches for selecting the training dataset...
Robotic tactile recognition aims at identifying target objects or environments from tactile sensory readings. The advancement of unsupervised feature learning and biological tactile sensing inspire us proposing the model of 3T-RTCN that performs spatio-temporal feature representation and fusion for tactile recognition. It decomposes tactile data in...