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  • Ongoing project by Stefan Richthofer: Predictable Feature Analysis
Ongoing project by Stefan Richthofer: Predictable Feature Analysis

The motivation of this project is based on the idea to apply the unsupervised learning algorithm Slow Feature Analysis (SFA) to interactive scenarios. This idea is based on the experience that SFA was successfully used in various (passive) analysis tasks that closely relate to interactive control scenarios, e.g. learning place cells, identifying objects invariant under spacial transformations, blind source separation, visual tasks like face recognition.

We recognize predictability as a crucial feature for tackling interactive scenarios, as these require estimation of consequences of possible actions.

Every organism in an environment, whether biological, robotic or virtual, must be able to predict certain aspects of its environment in order to survive or perform whatever task is intended. It needs a model that is capable of estimating the consequences of possible actions, so that planning, control, and decision-making become feasible. Predictable features are highly relevant for modeling, because predictability is a desired property of the needed consequence-estimating model by definition.

This motivates Predictable Feature Analysis (PFA), an algorithm strongly inspired by SFA: while SFA selects features by slowness, PFA selects them by predictability.



  • Singular Sturm-Liouville Problems with Zero Potential (q=0) and Singular Slow Feature Analysis
    Richthofer, S., & Wiskott, L.
    CoRR e-print arXiv:2011.04765
  • 2018

  • Global Navigation Using Predictable and Slow Feature Analysis in Multiroom Environments, Path Planning and Other Control Tasks
    Richthofer, S., & Wiskott, L.
    CoRR e-print arXiv:1805.08565
  • 2017

  • PFAx: Predictable Feature Analysis to Perform Control
    Richthofer, S., & Wiskott, L.
    CoRR e-print arXiv:1712.00634
  • 2015

  • Using SFA and PFA to solve navigation tasks in multi room environments
    Richthofer, S.
    Weekly Seminar talk, Institut für Neuroinformatik, Ruhr-Universität Bochum, Apr 1st, 2015, Bochum, Germany
  • Predictable Feature Analysis
    Richthofer, S., & Wiskott, L.
    In 14th IEEE International Conference on Machine Learning and Applications, ICMLA 2015, Miami, FL, USA, December 9-11, 2015 (pp. 190–196)
  • Predictable Feature Analysis
    Richthofer, S., & Wiskott, L.
    In Workshop New Challenges in Neural Computation 2015 (NC2) (pp. 68–75)
  • 2013

  • Predictable Feature Analysis
    Richthofer, S., & Wiskott, L.
    CoRR e-print arXiv:1311.2503
  • 2012

  • Predictable Feature Analysis
    Richthofer, S., Weghenkel, B., & Wiskott, L.
    In Frontiers in Computational Neuroscience

The Institut für Neuroinformatik (INI) is a central research unit of the Ruhr-Universität Bochum. We aim to understand the fundamental principles through which organisms generate behavior and cognition while linked to their environments through sensory systems and while acting in those environments through effector systems. Inspired by our insights into such natural cognitive systems, we seek new solutions to problems of information processing in artificial cognitive systems. We draw from a variety of disciplines that include experimental approaches from psychology and neurophysiology as well as theoretical approaches from physics, mathematics, electrical engineering and applied computer science, in particular machine learning, artificial intelligence, and computer vision.

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