Difference between revisions of "hamming Distance Metric Learning"

$b(x,w)=sign(f(w,x))$
b(x,w)=sign(...) In a previous paper, the authors tried to used a loss function which bears some similarity to the hinge function used in SVM. It includes a hyper-parameter which is a threshold in Hamming space that differentiates neighbors from non-neighbors. such that similar points are mapped to binary codes that do differ in more than P bits and disimilar points should map to points closer no more than P bits. For two binary codes $h$ and $g$ with hamming distance $||h-g||$ and a similarity label $s \in {0,1}$ the pairwise hinge loss function is defined as: