Nonnegative Signal Decomposition with Supervision
Keyword(s):
This paper presents a novel algorithm to numerically decompose mixed signals in a collaborative way, given supervision of the labels that each signal contains. The decomposition is formulated as an optimization problem incorporating nonnegative constraint. A nonnegative data factorization solution is presented to yield the decomposed results. It is shown that the optimization is efficient and decreases the objective function monotonically. Such a decomposition algorithm can be applied on multilabel training samples for pattern classification. The real-data experimental results show that the proposed algorithm can significantly facilitate the multilabel image classification performance with weak supervision.
2016 ◽
Vol XLI-B7
◽
pp. 443-449
2016 ◽
Vol XLI-B7
◽
pp. 443-449
◽
2019 ◽
Vol 13
◽
pp. 174830261988139