To balance the learning process between the overall semantics and the relation-specific information, we adopt an alternating optimization strategy 3 (Bezdek and Hathaway, 2003) to adjust λ based on different stage of the training instead of using a fixed weight for λ.
specifically, considering c is more reliable and possesses more information while u is learned with shared c, we alter-natively update c and u upon the convergence of c. we set λ to 1 in the first half of the training process and 0 afterwards.