How to find diversified vectors in a given set?

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I will describe my problem in details. I have a set of $N$ vectors, each of them defines a logistic regresion model. From this $N$ vectors (models) I want to take $n$ which are the most unique.

I'm not interested in similar vectors (models) because they share same information, and they will produce similar predictions. All of my $N$ models have high predictive power and I want to create single clasifier from $n$ most independent in a given set.

It is useles to make single calsifier $c=(A+B)/2$ from model A (predictive power=75%), and model $B$ (predictive power=75%) when they are equal ($A=B$).

But if they differ in predictor variables (defined in my vectors) and still have predictive power at high level (75%) in case when $Aneq B$ then combined clasifier $c=(A+B)/2$ will perform much better.

Is there any algoritmic (R), or mathematical framework for finding most independent (diversified?) subset of vectors in a given set?

linear-algebra vectors machine-learning

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edited Jul 24 at 11:18

Javi

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asked Jul 24 at 11:10

Łukasz Czop

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up vote
1
down vote

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I will describe my problem in details. I have a set of $N$ vectors, each of them defines a logistic regresion model. From this $N$ vectors (models) I want to take $n$ which are the most unique.

I'm not interested in similar vectors (models) because they share same information, and they will produce similar predictions. All of my $N$ models have high predictive power and I want to create single clasifier from $n$ most independent in a given set.

It is useles to make single calsifier $c=(A+B)/2$ from model A (predictive power=75%), and model $B$ (predictive power=75%) when they are equal ($A=B$).

But if they differ in predictor variables (defined in my vectors) and still have predictive power at high level (75%) in case when $Aneq B$ then combined clasifier $c=(A+B)/2$ will perform much better.

Is there any algoritmic (R), or mathematical framework for finding most independent (diversified?) subset of vectors in a given set?

linear-algebra vectors machine-learning

share|cite|improve this question

edited Jul 24 at 11:18

Javi

2,1481725

asked Jul 24 at 11:10

Łukasz Czop

61

add a comment | 

up vote
1
down vote

favorite

up vote
1
down vote

favorite

I will describe my problem in details. I have a set of $N$ vectors, each of them defines a logistic regresion model. From this $N$ vectors (models) I want to take $n$ which are the most unique.

I'm not interested in similar vectors (models) because they share same information, and they will produce similar predictions. All of my $N$ models have high predictive power and I want to create single clasifier from $n$ most independent in a given set.

It is useles to make single calsifier $c=(A+B)/2$ from model A (predictive power=75%), and model $B$ (predictive power=75%) when they are equal ($A=B$).

But if they differ in predictor variables (defined in my vectors) and still have predictive power at high level (75%) in case when $Aneq B$ then combined clasifier $c=(A+B)/2$ will perform much better.

Is there any algoritmic (R), or mathematical framework for finding most independent (diversified?) subset of vectors in a given set?

linear-algebra vectors machine-learning

share|cite|improve this question

edited Jul 24 at 11:18

Javi

2,1481725

asked Jul 24 at 11:10

Łukasz Czop

61

I will describe my problem in details. I have a set of $N$ vectors, each of them defines a logistic regresion model. From this $N$ vectors (models) I want to take $n$ which are the most unique.

I'm not interested in similar vectors (models) because they share same information, and they will produce similar predictions. All of my $N$ models have high predictive power and I want to create single clasifier from $n$ most independent in a given set.

It is useles to make single calsifier $c=(A+B)/2$ from model A (predictive power=75%), and model $B$ (predictive power=75%) when they are equal ($A=B$).

But if they differ in predictor variables (defined in my vectors) and still have predictive power at high level (75%) in case when $Aneq B$ then combined clasifier $c=(A+B)/2$ will perform much better.

Is there any algoritmic (R), or mathematical framework for finding most independent (diversified?) subset of vectors in a given set?

linear-algebra vectors machine-learning

share|cite|improve this question

edited Jul 24 at 11:18

Javi

2,1481725

asked Jul 24 at 11:10

Łukasz Czop

61

share|cite|improve this question

share|cite|improve this question

edited Jul 24 at 11:18

Javi

2,1481725

edited Jul 24 at 11:18

Javi

2,1481725

edited Jul 24 at 11:18

Javi

2,1481725

2,1481725

asked Jul 24 at 11:10

Łukasz Czop

61

asked Jul 24 at 11:10

Łukasz Czop

61

asked Jul 24 at 11:10

Łukasz Czop

61

61

add a comment | 

add a comment | 

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