Neural Comput - Robust subspace discovery via relaxed rank minimization.

Tópicos

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Resumo

This letter examines the problem of robust subspace discovery from input data samples (instances) in the presence of overwhelming outliers and corruptions. A typical example is the case where we are given a set of images; each image contains, for example, a face at an unknown location of an unknown size; our goal is to identify or detect the face in the image and simultaneously learn its model. We employ a simple generative subspace model and propose a new formulation to simultaneously infer the label information and learn the model using low-rank optimization. Solving this problem enables us to simultaneously identify the ownership of instances to the subspace and learn the corresponding subspace model. We give an efficient and effective algorithm based on the alternating direction method of multipliers and provide extensive simulations and experiments to verify the effectiveness of our method. The proposed scheme can also be used to tackle many related high-dimensional combinatorial selection problems.

Resumo Limpo

letter examin problem robust subspac discoveri input data sampl instanc presenc overwhelm outlier corrupt typic exampl case given set imag imag contain exampl face unknown locat unknown size goal identifi detect face imag simultan learn model employ simpl generat subspac model propos new formul simultan infer label inform learn model use lowrank optim solv problem enabl us simultan identifi ownership instanc subspac learn correspond subspac model give effici effect algorithm base altern direct method multipli provid extens simul experi verifi effect method propos scheme can also use tackl mani relat highdimension combinatori select problem

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