Learning Hierarchical Sparse Representations using Iterative Dictionary Learning and Dimension Reduction

Autor:	Tarifi, Mohamad, Sitharam, Meera, Ho, Jeffery
Rok vydání:	2011
Předmět:	Computer Science - Learning Computer Science - Artificial Intelligence Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	This paper introduces an elemental building block which combines Dictionary Learning and Dimension Reduction (DRDL). We show how this foundational element can be used to iteratively construct a Hierarchical Sparse Representation (HSR) of a sensory stream. We compare our approach to existing models showing the generality of our simple prescription. We then perform preliminary experiments using this framework, illustrating with the example of an object recognition task using standard datasets. This work introduces the very first steps towards an integrated framework for designing and analyzing various computational tasks from learning to attention to action. The ultimate goal is building a mathematically rigorous, integrated theory of intelligence.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1106.0357 Zobrazit plný text záznamu View this record from Arxiv