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| Integrating concepts at the intersection of algebra & geometry could provide better machine learning algorithms, Photo Credit: Max Fischer |
NEW DELHI (LisbonTimes):- Scientists may soon
develop robust algorithms that can provide more efficient machine learning
applications by focusing on concepts that lie at the intersection of algebra and geometry.
Hariharan Narayanan, Assistant
Professor, Tata Institute of Fundamental Research Mumbai, a
recipient of this year’s SwarnaJayanti fellowship instituted by the Department
of Science & Technology, Govt. of India, wishes to create machine learning
algorithms that can learn from observations and make improved predictions based
on mathematical objects known as manifolds and Lie groups. This can lead to
improved modeling of data arising from certain sources, such as visual
observations.
Machine learning can
be broadly defined as a discipline whose goal is to enable a computer to make
inferences from observed data about future observations. There are two
directions in which progress is crucial to make progress in machine learning.
The first is making inferences from very few observations. The second is
dealing with complex data, which has come to prominence through recent
applications in vision, imaging like Cryo-electron Microcrope and the World
Wide Web.
The use of manifolds and Lie groups can help to address both of these issues and may lead to algorithms that make better predictions in real-life applications.
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