Mathematic explanation needed for a univariate classification method based on solving a quadratic equation












0












$begingroup$


I read a piece of codes on classifying image hue values into three classes with derived thresholds. The thresholds are calculated by simply using a quadratic formula. The related documentations for the codes are as follows:



/*
* (v_B - v_A)x^2 + 2(m_B*v_A - m_A*v_B)x + (m_A^2*v_B - m_B^2*v_A) = 0
*
* v_A : variance of group A
* m_A : mean of group A
* v_B : variance of group B
* m_B : mean of group B
*
* Quadratic formula
* ax^2 + bx + c = 0
* a = v_B - v_A
* b = 2(m_B*v_A - m_A*v_B)
* c = (m_A^2*v_B - m_B^2*v_A)
*
* x1 = (-b+sqrt(b^2-4ac))/2a
* x2 = (-b-sqrt(b^2-4ac))/2a
*
*/


Here above is to find a threshold to delimit the class A and B, based on solving a quadratic equation involving the mean and variance of the two groups, and the threshold is one of the solutions (i.e., x1 and x2) depending on their signs.



I haven't seen such a quadratic equation based classification method before. Anyone know what formal name for the method? It, on the first sight, seems to me a simplified case of quadratic discriminant analysis, but not sure about that. I need a little bit mathematic background for the method. Educate me if you know that. Thanks!










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    0












    $begingroup$


    I read a piece of codes on classifying image hue values into three classes with derived thresholds. The thresholds are calculated by simply using a quadratic formula. The related documentations for the codes are as follows:



    /*
    * (v_B - v_A)x^2 + 2(m_B*v_A - m_A*v_B)x + (m_A^2*v_B - m_B^2*v_A) = 0
    *
    * v_A : variance of group A
    * m_A : mean of group A
    * v_B : variance of group B
    * m_B : mean of group B
    *
    * Quadratic formula
    * ax^2 + bx + c = 0
    * a = v_B - v_A
    * b = 2(m_B*v_A - m_A*v_B)
    * c = (m_A^2*v_B - m_B^2*v_A)
    *
    * x1 = (-b+sqrt(b^2-4ac))/2a
    * x2 = (-b-sqrt(b^2-4ac))/2a
    *
    */


    Here above is to find a threshold to delimit the class A and B, based on solving a quadratic equation involving the mean and variance of the two groups, and the threshold is one of the solutions (i.e., x1 and x2) depending on their signs.



    I haven't seen such a quadratic equation based classification method before. Anyone know what formal name for the method? It, on the first sight, seems to me a simplified case of quadratic discriminant analysis, but not sure about that. I need a little bit mathematic background for the method. Educate me if you know that. Thanks!










    share|improve this question









    New contributor




    jingweimo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$















      0












      0








      0





      $begingroup$


      I read a piece of codes on classifying image hue values into three classes with derived thresholds. The thresholds are calculated by simply using a quadratic formula. The related documentations for the codes are as follows:



      /*
      * (v_B - v_A)x^2 + 2(m_B*v_A - m_A*v_B)x + (m_A^2*v_B - m_B^2*v_A) = 0
      *
      * v_A : variance of group A
      * m_A : mean of group A
      * v_B : variance of group B
      * m_B : mean of group B
      *
      * Quadratic formula
      * ax^2 + bx + c = 0
      * a = v_B - v_A
      * b = 2(m_B*v_A - m_A*v_B)
      * c = (m_A^2*v_B - m_B^2*v_A)
      *
      * x1 = (-b+sqrt(b^2-4ac))/2a
      * x2 = (-b-sqrt(b^2-4ac))/2a
      *
      */


      Here above is to find a threshold to delimit the class A and B, based on solving a quadratic equation involving the mean and variance of the two groups, and the threshold is one of the solutions (i.e., x1 and x2) depending on their signs.



      I haven't seen such a quadratic equation based classification method before. Anyone know what formal name for the method? It, on the first sight, seems to me a simplified case of quadratic discriminant analysis, but not sure about that. I need a little bit mathematic background for the method. Educate me if you know that. Thanks!










      share|improve this question









      New contributor




      jingweimo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      I read a piece of codes on classifying image hue values into three classes with derived thresholds. The thresholds are calculated by simply using a quadratic formula. The related documentations for the codes are as follows:



      /*
      * (v_B - v_A)x^2 + 2(m_B*v_A - m_A*v_B)x + (m_A^2*v_B - m_B^2*v_A) = 0
      *
      * v_A : variance of group A
      * m_A : mean of group A
      * v_B : variance of group B
      * m_B : mean of group B
      *
      * Quadratic formula
      * ax^2 + bx + c = 0
      * a = v_B - v_A
      * b = 2(m_B*v_A - m_A*v_B)
      * c = (m_A^2*v_B - m_B^2*v_A)
      *
      * x1 = (-b+sqrt(b^2-4ac))/2a
      * x2 = (-b-sqrt(b^2-4ac))/2a
      *
      */


      Here above is to find a threshold to delimit the class A and B, based on solving a quadratic equation involving the mean and variance of the two groups, and the threshold is one of the solutions (i.e., x1 and x2) depending on their signs.



      I haven't seen such a quadratic equation based classification method before. Anyone know what formal name for the method? It, on the first sight, seems to me a simplified case of quadratic discriminant analysis, but not sure about that. I need a little bit mathematic background for the method. Educate me if you know that. Thanks!







      classification






      share|improve this question









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      jingweimo is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.











      share|improve this question









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      Check out our Code of Conduct.









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      Check out our Code of Conduct.






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