Aiming at the problem of the error existed from the hidden layer to the output layer of Extreme Learning Machine(ELM), it was found the analysis revealed that the error came from the process of solving the Moore-Penrose generalized inverse matrix H ^† of the hidden layer output matrix H,that revaled the matrix H ^† H was deviated from the identity matrix. The appropriate output matrix H was able to be selected according to the degree of deviation to obtain a smaller training error. According to the definitions of the generalized inverse matrix and auxiliary matrix,the target matrix H ^† H and the error index L21-norm were firstly determined. Then,the experimental analysis showed that the L21-norm of H ^† H was linearly related to the ELM error. Finally,Gaussian filtering was introduced to reduce the noise of the target matrix,which effectively reduced the L21-norm of the target matrix and the ELM error,achieving the purpose of optimizing the ELM algorithm.

While Twitter and Sina micro-blogs abundant registered users formed a social network of focusing relationship, by using the degree of symmetry its change regulation with the scale of the social circle was studied. Firstly, based on the collection of 1000000 focusing relationships among the Sina micro-blog users and 236 Twitter users as well as their focusing relationships, the initial social network was established. Here focus lied on the connected sub-networks which had obvious symmetrical connects, then the elimination method was applied to obtain these conclusions: The major factors that affect the symmetry of the maximum connected sub-networks are those who are called big V users and negligible users. After that, comparative analysis method was used to find out that the sub-network consisted of the big V users in Twitter has a stronger symmetry. Finally, the difference between these two kinds of micro-blogs was figured out in terms of functional localization. Through the researches on the symmetry of all connected sub-networks within the initial network, the result shows that when the scale of a public social circle decreases, the corresponding symmetry becomes stronger.