Abstract
This paper presents a new algorithm to find several types of extreme points of higher dimensional lattice polytopes enclosing a given finite set as derived from the canonical min/max lattice autoassociative memories. The algorithm first computes the basic extreme points that include the corners of the hyperbox containing the data together with the translated min/max points. Then, the algorithm computes additional extreme points such as entry or exit line points from the basic ones. Using convex geometry and lattice algebra, we discuss the rationale of the proposed technique with simple illustrative examples.
G. Urcid thanks SNI-CONACYT for partial financial support, grant # 22036.
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Ritter, G.X., Urcid, G. (2018). Extreme Points of Convex Polytopes Derived from Lattice Autoassociative Memories. In: Martínez-Trinidad, J., Carrasco-Ochoa, J., Olvera-López, J., Sarkar, S. (eds) Pattern Recognition. MCPR 2018. Lecture Notes in Computer Science(), vol 10880. Springer, Cham. https://doi.org/10.1007/978-3-319-92198-3_12
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DOI: https://doi.org/10.1007/978-3-319-92198-3_12
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