Lower and Upper Bounds on the VC-Dimension of Tensor Network Models
Behnoush Khavari,
Guillaume Rabusseau
Abstract:Tensor network (TN) methods have been a key ingredient of advances in condensed matter physics and have recently sparked interest in the machine learning community for their ability to compactly represent very high-dimensional objects. TN methods can for example be used to efficiently learn linear models in exponentially large feature spaces [54]. In this work, we derive upper and lower bounds on the VC-dimension and pseudo-dimension of a large class of TN models for classification, regression and completion. … Show more
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