The Importance of Structure

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• 刊名：Springer Tracts in Advanced Robotics
• 出版年：2017
• 出版时间：2017
• 年：2017
• 卷：100
• 期：1
• 页码：111-127
• 全文大小：682 KB
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• 作者单位：Carl Henrik Ek (5)
  Danica Kragic (6)
  
  5. University of Bristol, Bristol, UK
  6. Royal Institute of Technology, Stockholm, Sweden
  
• 丛书名：Robotics Research
• ISBN：978-3-319-29363-9
• 刊物类别：Engineering
• 刊物主题：Automation and Robotics
  Control Engineering
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1610-742X
• 卷排序：100

文摘

Many tasks in robotics and computer vision are concerned with inferring a continuous or discrete state variable from observations and measurements from the environment. Due to the high-dimensional nature of the input data the inference is often cast as a two stage process: first a low-dimensional feature representation is extracted on which secondly a learning algorithm is applied. Due to the significant progress that have been achieved within the field of machine learning over the last decade focus have placed at the second stage of the inference process, improving the process by exploiting more advanced learning techniques applied to the same (or more of the same) data. We believe that for many scenarios significant strides in performance could be achieved by focusing on representation rather than aiming to alleviate inconclusive and/or redundant information by exploiting more advanced inference methods. This stems from the notion that; given the “correct” representation the inference problem becomes easier to solve. In this paper we argue that one important mode of information for many application scenarios is not the actual variation in the data but the rather the higher order statistics as the structure of variations. We will exemplify this through a set of applications and show different ways of representing the structure of data.