Babel function

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The Babel function (also known as cumulative coherence) measures the maximum total coherence between a fixed and a collection of other atoms in a . The Babel function was conceived of in the context of signals for which there exists a sparse representation consisting of atoms or columns of a redundant dictionary matrix, A.

Definition and formulation[]

The Babel function of a dictionary ${\displaystyle {\boldsymbol {A}}}$ with normalized columns is a real-valued function that is defined as

${\displaystyle \mu _{1}(p)=\max _{|\lambda |=p}\{\max _{j\notin \lambda }\{\sum _{i\in \lambda }{|{\boldsymbol {a}}_{i}^{\boldsymbol {T}}{\boldsymbol {a}}_{j}|}\}\}}$

where ${\displaystyle {\boldsymbol {a}}_{k}}$ are the columns (atoms) of the dictionary ${\displaystyle {\boldsymbol {A}}}$.^[1][2]

Special case[]

When p=1, the babel function is the mutual coherence.

Pratical Applications[]

Li and Lin have used the Babel function to aid in creating effective dictionaries for Machine Learning applications.^[3]

References[]

See also[]

• Compressed sensing