Visualization of Learning Process in Feature Space

In machine learning, the structure of feature space is an important factor that determines performance a model. Therefore, we can deepen our understanding learning algorithms if visualize changes in during process. However, visualizing such difficult because it requires dimensionality reduction while maintaining consistency with data high-dimensional and temporal direction. this study, visualized process by capturing them as positional relationship between target features time-invariant reference coordinates log-bilinear