What is the name for this classification algorithm?












1















Can you help me find the name of this classification algorithm :



Assume we have data:
$n$ dimensional feature vectors we want to classify in two classes.

We model the classes as two $n$ dimensional gaussian distributions estimated from the data.

We classify a new vector to the class that maximizes the PDF(probability density function) at that point.






classification normal-distribution multivariate-analysis pdf algorithms







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  • Two component Gaussian mixture model?

    – Bey
    19 mins ago
















1















Can you help me find the name of this classification algorithm :



Assume we have data:
$n$ dimensional feature vectors we want to classify in two classes.

We model the classes as two $n$ dimensional gaussian distributions estimated from the data.

We classify a new vector to the class that maximizes the PDF(probability density function) at that point.






classification normal-distribution multivariate-analysis pdf algorithms







share|cite|improve this question























  • Two component Gaussian mixture model?

    – Bey
    19 mins ago














1












1








1


1






Can you help me find the name of this classification algorithm :



Assume we have data:
$n$ dimensional feature vectors we want to classify in two classes.

We model the classes as two $n$ dimensional gaussian distributions estimated from the data.

We classify a new vector to the class that maximizes the PDF(probability density function) at that point.






classification normal-distribution multivariate-analysis pdf algorithms







share|cite|improve this question














Can you help me find the name of this classification algorithm :



Assume we have data:
$n$ dimensional feature vectors we want to classify in two classes.

We model the classes as two $n$ dimensional gaussian distributions estimated from the data.

We classify a new vector to the class that maximizes the PDF(probability density function) at that point.






classification normal-distribution multivariate-analysis pdf algorithms




classification normal-distribution multivariate-analysis pdf algorithms






share|cite|improve this question













share|cite|improve this question











share|cite|improve this question




share|cite|improve this question










asked 3 hours ago









SoloNasusSoloNasus

1585




1585













  • Two component Gaussian mixture model?

    – Bey
    19 mins ago



















  • Two component Gaussian mixture model?

    – Bey
    19 mins ago

















Two component Gaussian mixture model?

– Bey
19 mins ago





Two component Gaussian mixture model?

– Bey
19 mins ago










1 Answer
1






active

oldest

votes


















2














Probably Quadratic Discriminant Analysis.



There are also names for different constraints you could make:




  1. Covariance matrices of both classes are equal - Linear Discriminant Analysis.


  2. Only diagonal elements of the covariance matrix are non-zero - Naive Bayes Classifier


  3. Covariance matrix is identity (diagonals = 1, non-diagonals = 0) - Nearest Centroid Classifier







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    1 Answer
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    1 Answer
    1






    active

    oldest

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    active

    oldest

    votes






    active

    oldest

    votes









    2














    Probably Quadratic Discriminant Analysis.



    There are also names for different constraints you could make:




    1. Covariance matrices of both classes are equal - Linear Discriminant Analysis.


    2. Only diagonal elements of the covariance matrix are non-zero - Naive Bayes Classifier


    3. Covariance matrix is identity (diagonals = 1, non-diagonals = 0) - Nearest Centroid Classifier







    share|cite|improve this answer






























      2














      Probably Quadratic Discriminant Analysis.



      There are also names for different constraints you could make:




      1. Covariance matrices of both classes are equal - Linear Discriminant Analysis.


      2. Only diagonal elements of the covariance matrix are non-zero - Naive Bayes Classifier


      3. Covariance matrix is identity (diagonals = 1, non-diagonals = 0) - Nearest Centroid Classifier







      share|cite|improve this answer




























        2












        2








        2







        Probably Quadratic Discriminant Analysis.



        There are also names for different constraints you could make:




        1. Covariance matrices of both classes are equal - Linear Discriminant Analysis.


        2. Only diagonal elements of the covariance matrix are non-zero - Naive Bayes Classifier


        3. Covariance matrix is identity (diagonals = 1, non-diagonals = 0) - Nearest Centroid Classifier







        share|cite|improve this answer















        Probably Quadratic Discriminant Analysis.



        There are also names for different constraints you could make:




        1. Covariance matrices of both classes are equal - Linear Discriminant Analysis.


        2. Only diagonal elements of the covariance matrix are non-zero - Naive Bayes Classifier


        3. Covariance matrix is identity (diagonals = 1, non-diagonals = 0) - Nearest Centroid Classifier








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        edited 3 hours ago

























        answered 3 hours ago









        Karolis KoncevičiusKarolis Koncevičius

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        1,74921425






























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