Embedding viaclustering: Using spectral information toguidedimensionality reduction
We describe anapproach toimprove iterative di- mensionality reduction methods byusing information contained intheleading eigenvectors ofa dataaffinity matrix. Using an insight fromtheareaofspectral clustering, we suggest modifying thegradient ofaniterative method, sothatlatent spaceelements belonging tothesamecluster areencouraged to moveinsimilar directions during optimization. Wealso describe waytoachieve this without actually having toexplicitly perform aneigendecomposition. Preliminary experiments showthat our approach makesitpossible tospeed upiterative methods and helps themtofind better local minima oftheir objective function.