Discovering relevant sensor data by Qanalysis

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Discovering relevant sensor data by Q-analysis

• Pejman Iravani
• Department of Design and Innovation
• The Open University

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Introduction

• The problem of adding new sensors
• In principle, adding sensors to a robot seems
  beneficial as more information is given to the
  robot.
• But, this is not the case if the sensors added
  provide irrelevant or redundant data. In which
  case adding sensors has negative impact due to
  the curse of dimensionality.

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Introduction

• Curse of dimensionality is related to the
  exponential growth of a hyperspace as a function
  of its dimensions.

Adding irrelevant sensors increases exponentially
the size of the state space and thus the
computational requirements of the system dealing
with such space.
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The feature selection problem

• Given a set of m features (robot sensors) select
  a subset of n relevant features, where mgtn, such
  that the subset provides the same or similar
  information than the original set.

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The feature selection problem

• The relevance of the features is assessed in
  relation to classification.
• A feature is relevant if it provides useful
  information for discriminating entities which
  belong to different classes.

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Classification by Q-analysis

• Given a robot with some binary sensors.
• At any point in time a number of sensors will be
  active (e.g. pressed bumpers), which are
  represented by simplices s.

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Classification by Q-analysis
sltx1,x2,x3gt
sltx1,x2,x3,x4gt

• The dimension of the shared face is known as the
  q-nearness of two simplices.
• A fundamental hypothesis is that q-nearness is a
  measure of structural similarity.

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Classification by Q-analysis

• The idea of q-nearness can be extended to a set
  of simplices, in which case, their largest shared
  face is known as hub.

Classification using Q-analysis is based on
finding relevant hubs with respect to each
class. The vertices that are not included in the
hubs can be considered as irrelevant.
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Feature selection by Q-analysis