Learning using the Born Rule
In Quantum Mechanics the transition from a deterministic descriptionto a probabilistic one is done using a simple rule termed the Bornrule. This rule states that the probability of an outcome ($a$)given a state ($\Psi$) is the square of their inner products($(a^\top\Psi)^2$).In this paper, we unrave...
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| Language: | en_US |
| Published: |
2006
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| Online Access: | http://hdl.handle.net/1721.1/32978 |
| Summary: | In Quantum Mechanics the transition from a deterministic descriptionto a probabilistic one is done using a simple rule termed the Bornrule. This rule states that the probability of an outcome ($a$)given a state ($\Psi$) is the square of their inner products($(a^\top\Psi)^2$).In this paper, we unravel a new probabilistic justification forpopular algebraic algorithms, based on the Born rule. Thesealgorithms include two-class and multiple-class spectral clustering,and algorithms based on Euclidean distances. |
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