Frequency-domain features reveal the activated patterns of individual local brain regions responding to different emotions, whereas brain connectivity features involve the coordination of multiple brain regions for generating emotional responses; these two types of features are complementary to each other. To date, the fusion of these two types of features for EEG-based cross-subject emotion recognition remains to be fully investigated due to the inter-subject variability in EEG signals. In this paper, we first attempt to investigate these fused features for cross-subject emotion recognition from multiple perspectives, including critical frequency bands, complementary characteristics for each emotional state, critical channels, and crucial connections, using a fast and robust approximate empirical kernel map-fusion-based support vector machine (AEKM-Fusion-SVM) method. The experimental results on the SEED, BCI2020-A, and BCI2020-B datasets reveal that: 1) the AEKM-fusion method improves the effectiveness and efficiency of the fusion of features of different dimensions; 2) the recognition accuracy of the fused features outperforms each individual feature, and this outperformance is more significant in the high-frequency bands (i.e., the beta and gamma bands);3) the fused features significantly enhance the classification performance for negative emotion; and 4) the fused features built with 27 selected channels achieve comparable performance to that of the fused features built with the full number of channels (i.e., 62 channels), allowing for easier establishment of BCI systems in real-world scenarios. Our study enriches the research of emotion-related brain mechanisms and also provides new insight into affective computing.