To combat the missing value problem in terrorism behavior data set, this paper proposed Compressed Context Space (CCS) method which is based on the idea of maximizing the dependence between the context vectors and actions. CCS relied on Hilbert-Schmidt independence criterion which evaluated the relationship between two variables according to their Hilbert-Schmidt norm. Theories have proven Hilbert-Schmidt norm can detect dependence. In order to detect the relevance well and maximum the dependence between the context features and actions, CCS should maximum Hilbert-Schmidt norm between the linearly mapped low-dimensional features and actions, which is able to reduce the effect of missing value problem. Combining CCS followed SVM (CCS) can produce effective classification. Experiments on MAROB show that the proposed CCS+SVM improves SVM, PCA+SVM, CCA+SVM and CONVEX by at least 1.5% and 1.0% for recall and F measure, and has competitive performance with the best results for precision and Area Under ROC Curve (AUC). The results show that CCS+SVM handles missing value problem well.