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Learning Subjective Representations Through Dimensionality Reduction

posted Sep 2, 2008, 10:41 AM by Brian Tanner   [ updated Sep 2, 2008, 11:11 AM ]

Dana Wilkinson and Michael Bowling and Ali Ghodsi

There are a variety of domains where one wishes to learn a representation of an environment defined by a stream of sensori-motor experience.  In many cases, such a representation is necessary as the observational data is too plentiful to be stored in a computationally feasible way.  In other words, the primary feature of a learned representation is that it must be compact---summarizing information in a way that alleviates storage demands.

This admits a new way of phrasing the problem: as a variation of dimensionality reduction.  There are a variety of well-studied algorithms for the dimensionality reduction problem, we argue that any of these can be useful for learning compact representations as long as additional constraints to the problem are respected---namely that the result is useful for reasoning and planning.

Here, we formalize the problem of learning a subjective representation, clearly articulating solution features that are necessary for a learned representation to be ``useful''.  Further, we briefly present a possible solution to the newly defined problem (Action Respecting Embedding) and demonstrate it's effectiveness.