Current graph embedding frameworks of supervised dimensionality reduction often preserve the intraclass local structures and maximize the interclass variance. However, this strategy fails to provide adequate results when strict within-class multimodalities contradict between-class separations. In this paper, we propose Hypersphere Distribution Discriminant Analysis (HDDA), which determines the affinity by considering not only within-class local structure but also the heteropoint distribution in the neighborhood space. If the heteropoint distribution is relatively high in the feature space, this pair should be mapped apart to avoid mixing problems. By taking both the distribution of heteropoints and the distance into account, HDDA shows more effective results compared to the state-of-the-art methods.