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where [[rho]]...[[rho]]X,Y W 0 which implies using the expression for the partial correlation that [[rho]]X,Y [[rho]]X,W [[rho]]Y,W Since [[rho]]X,Y <1,[[rho]]X,W <1,[[rho]]Y,W <1 this in turn implies that under the hypothesis of conditional independence [[rho]]X,Y <[[rho]]X,W or [[rho]]Y,W Hence if W is a random variable representing relevance then thecorrelation between it and either index term is greater than the correlation between the index terms
...Qualitatively I shall try and generalise this to functions other than correlation coefficients,Linfott [27]defines a type of informational correlation measure by rij 1 exp 2 I xi,xj [1 2]0 <rij <1 or where I xi,xj is the now familiar expected mutual information measure
...I xi,xj <I xi,W or I xj,W,where I
...Discrimination Gain Hypothesis:Under the hypothesis ofconditional independence the statistical information contained in oneindex term about another is less than the information contained ineither index term about relevance
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the different contributions made to the measure by the different cells
...Discrimination gain hypothesis In the derivation above I have made the assumption of independence or dependence in a straightforward way
...P xi,xj P xi,xj w 1 P w 1 P xi,xi w 2 P w 2 P xi P xj [P xi w 1 P w 1 P xi,w 2 P w 2][P xj w 1 P w 1 P xj,w 2 P w 2]If we assume conditional independence on both w 1 and w 2 then P xi,xj P xi,w 1 P xj,w 1 P w 1 P xi w 2 P xj w 2 P w 2 For unconditional independence as well,we must have P xi,xj P xi P xj This will only happen when P w 1 0 or P w 2 0,or P xi w 1 P xi w 2,or P xj w 1 P xj w 2,or in words,when at least one of the index terms is useless at discriminating relevant from non relevant documents
...Kendall and Stuart [26]define a partial correlation coefficient for any two distributions by |
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