The purpose of the tensor-on-tensor regression, which we examine, is to relate tensor responses to tensor covariates with a low Tucker rank parameter tensor/matrix without being aware of its intrinsic rank beforehand.

By examining the impact of rank over-parameterization, we suggest the Riemannian Gradient Descent (RGD) and Riemannian Gauss-Newton (RGN) methods to address the problem of unknown rank. By demonstrating that RGD and RGN, respectively, converge linearly and quadratically to a statistically optimal estimate in both rank correctly-parameterized and over-parameterized scenarios, we offer the first convergence guarantee for the generic tensor-on-tensor regression. According to our theory, Riemannian optimization techniques automatically adjust to over-parameterization without requiring implementation changes.

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7 0

1 year ago

This table shows the number of students at a school in the years 2014-2016, 2014 1,030 students 2015 ? students 2016 1,300 stude

vladimir1956 [14]

Answer:

it could be the number in 2015 is 2,330

Step-by-step explanation:

Just add the number of the students in 2014-2016.

1,030+ 1,300=2,330

4 0

2 years ago