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یادگیری ماشین 2013
جزئیات رویداد
همایش ، رویداد ، ژورنال
اینستاگرام تی پی بین
حوزه های تحت پوشش رویداد
  • جزئیات بیشتر مقاله
    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 1546
    • تعداد پرسش و پاسخ ها: 0
    • شماره تماس دبیرخانه رویداد: -
     we present a framework based on convex optimization and spectral regularization to perform learning when feature observations are multidimensional arrays (tensors). we give a mathematical characterization of spectral penalties for tensors and analyze a unifying class of convex optimization problems for which we present a provably convergent and scalable template algorithm. we then specialize this class of problems to perform learning both in a transductive as well as in an inductive setting. in the transductive case one has an input data tensor with missing features and, possibly, a partially observed matrix of labels. the goal is to both infer the missing input features as well as predict the missing labels. for induction, the goal is to determine a model for each learning task to be used for out of sample prediction. each training pair consists of a multidimensional array and a set of labels each of which corresponding to related but distinct tasks. in either case the proposed technique exploits precise low multilinear rank assumptions over unknown multidimensional arrays; regularization is based on composite spectral penalties and connects to the concept of multilinear singular value decomposition. as a by-product of using a tensor-based formalism, our approach allows one to tackle the multi-task case in a natural way. empirical studies demonstrate the merits of the proposed methods.

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