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The importance of structure
KTH, Superseded Departments, Numerical Analysis and Computer Science, NADA.ORCID iD: 0000-0003-2965-2953
2017 (English)In: 15th International Symposium of Robotics Research, 2011, Springer, 2017, 111-127 p.Conference paper (Refereed)
Abstract [en]

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. © Springer International Publishing Switzerland 2017.

Place, publisher, year, edition, pages
Springer, 2017. 111-127 p.
Keyword [en]
Artificial intelligence, Computer vision, Higher order statistics, Inference engines, Learning systems, Robotics, Advanced learning, Application scenario, Feature representation, Inference methods, Inference problem, Inference process, Observations and measurements, Two-stage process, Learning algorithms
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-195120DOI: 10.1007/978-3-319-29363-9_7ScopusID: 2-s2.0-84984823812ISBN: 9783319293622OAI: diva2:1048286
9 December 2011 through 12 December 2011

Correspondence Address: Ek, C.H.; University of BristolUnited Kingdom; email: QC 20161121

Available from: 2016-11-21 Created: 2016-11-02 Last updated: 2016-11-21Bibliographically approved

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Kragic, Danica
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