Instead of reducing and pruning possible feature concatenations (e.g., by manual feature template construction as in the traditional approach), this tensor scoring method avoids parameter explosion and overfitting by assuming a low-rank factorization of the parameters A.
a is decomposed into the sum of k simple rank-1 components, Here k is a small constant, P, Q ∈ R k×n , R ∈ R k×m and S ∈ R k×l are parameter matrices, and P (i) (and similarly Q(i), R(i) and S(i)) represents the i-th row vector of matrix P .