I'm trying to implement Integer Programming for Nearest Neighbor Classifier in python using cvxpy.

Short intro

Given a dataset of n points with a color (red or blue) we would like to choose the minimal number of candidate points, s.t for each point that isn`t a candidate, its closest candidate has the same color.

My flow

Given a set of n points (with colors) define an indicator vector I (|I| = n),

I_i = 1 if and only if vertex i is chosen as a candidate

In addition, I defined two more vectors, named as A and B (|A| = |B| = n) as follow:

A_i = the distance between v_i to it's closest candidate with the **same** color

B_i = the distance between v_i to it's closest candidate with a **different** color

Therefore, I have n constrains which are: B_i > A_i for any i

My target is to minimize the sum of vector I (which represents the number of candidates)

My Issue

Its seems that the vectors A, B are changing because they affected by I, since when a candidate is chosen, it is affecting its entry in I which affects A and B and the constrains are dependent on those vectors..

Any suggestions?

Thanks !

CodePudding user response：

To recap: you want to find the smallest set of examples belonging to a given training set such that the resulting nearest neighbor classifier achieves perfect accuracy on that training set.

I would suggest that you formulate this as follows. Create a 0–1 variable x(e) for each example e indicating whether e is chosen. For each ordered pair of examples e and e′ with different labels, write a constraint

x(e′) ≤ ∑_{e′′∈C(e,e′)} x(e′′)

where C(e, e′) is the set of examples e′′ with the same label as e such that e′′ is closer to e than e′ is to e (including e′′ = e). This means that, if e′ is chosen, then it is not the nearest chosen example to e.

We also need

∑_e x(e) ≥ 1

to disallow the empty set. Finally, the objective is

minimize ∑_e x(e).