Memoization for calculating minimal traversal distance within a positive matrix
Some documentation is sorely needed, both about the problem itself, and the approach taken to solve it.
Making y global is not a good idea. If you want to solve more than one matrix, the caller of this function would be responsible for resetting y between calls. Instead you could pass y around as an argument, perhaps initializing it like this:

def findLeastPath(matrix, y=None, row=0, column=0, path=[], sum_=0):
    if y is None:
        y = numpy.zeros_like(matrix)

I see that the possible moves are only right, down and diagonally down-right. The problem could be solved in a simple way by looping once through the array row by row. One can always easily compute the minimum cost of reaching the current node, because it is only possible to arrive from nodes whose cost has already been computed.
Dijkstra's algorithm is another option if costs are non-negative. Basically, always extend the shortest path by one step. Pro: may not have to visit all the nodes. Con: needs to maintain a priority queue.