@article{931390,
      author = {Zhao, Haitao, and Lai, Zhihui. and Leung, Henry. and  Zhang, Xianyi.},
      url = {http://library.usi.edu/record/931390},
      title = {Feature learning and understanding : algorithms and  applications /},
      publisher = {Springer,},
      abstract = {This book covers the essential concepts and strategies  within traditional and cutting-edge feature learning  methods thru both theoretical analysis and case studies.  Good features give good models and it is usually not  classifiers but features that determine the effectiveness  of a model. In this book, readers can find not only  traditional feature learning methods, such as principal  component analysis, linear discriminant analysis, and  geometrical-structure-based methods, but also advanced  feature learning methods, such as sparse learning, low-rank  decomposition, tensor-based feature extraction, and  deep-learning-based feature learning. Each feature learning  method has its own dedicated chapter that explains how it  is theoretically derived and shows how it is implemented  for real-world applications. Detailed illustrated figures  are included for better understanding. This book can be  used by students, researchers, and engineers looking for a  reference guide for popular methods of feature learning and  machine intelligence.},
      recid = {931390},
      pages = {1 online resource},
      address = {Cham :},
      year = {2020},
}