1 research outputs found
Learning through deterministic assignment of hidden parameters
Supervised learning frequently boils down to determining hidden and bright
parameters in a parameterized hypothesis space based on finite input-output
samples. The hidden parameters determine the attributions of hidden predictors
or the nonlinear mechanism of an estimator, while the bright parameters
characterize how hidden predictors are linearly combined or the linear
mechanism. In traditional learning paradigm, hidden and bright parameters are
not distinguished and trained simultaneously in one learning process. Such an
one-stage learning (OSL) brings a benefit of theoretical analysis but suffers
from the high computational burden. To overcome this difficulty, a two-stage
learning (TSL) scheme, featured by learning through deterministic assignment of
hidden parameters (LtDaHP) was proposed, which suggests to deterministically
generate the hidden parameters by using minimal Riesz energy points on a sphere
and equally spaced points in an interval. We theoretically show that with such
deterministic assignment of hidden parameters, LtDaHP with a neural network
realization almost shares the same generalization performance with that of OSL.
We also present a series of simulations and application examples to support the
outperformance of LtDaH