Batch Nuclear-norm Maximization
2000년 도입 · 논문 3편에서 사용
Batch Nuclear-norm Maximization is an approach for aiding classification in label insufficient situations. It involves maximizing the nuclear-norm of the batch output matrix. The nuclear-norm of a matrix is an upper bound of the Frobenius-norm of the matrix. Maximizing nuclear-norm ensures large Frobenius-norm of the batch matrix, which leads to increased discriminability. The nuclear-norm of the batch matrix is also a convex approximation of the matrix rank, which refers to the prediction diversity.
Regularization · General