How do we retrieve cartoon characters accurately? Or how to synthesize new cartoon clips smoothly and efficiently from the cartoon library? Both questions are important for animators and cartoon enthusiasts to design and create new cartoons by utilizing existing cartoon materials. The first key issue to answer those questions is to find a proper representation that describes the cartoon character effectively. In this paper, we consider multiple features from different views, i.e., color histogram, Hausdorff edge feature, and skeleton feature, to represent cartoon characters with different colors, shapes, and gestures. Each visual feature reflects a unique characteristic of a cartoon character, and they are complementary to each other for retrieval and synthesis. However, how to combine the three visual features is the second key issue of our application. By simply concatenating them into a long vector, it will end up with the so-called “curse of dimensionality,” let alone their heterogeneity embedded in different visual feature spaces. Here, we introduce a semisupervised multiview subspace learning (semi-MSL) algorithm, to encode different features in a unified space. Specifically, under the patch alignment framework, semi-MSL uses the discriminative information from labeled cartoon characters in the construction of local patches where the manifold structure revealed by unlabeled cartoon characters is utilized to capture the geometric distribution. The experimental evaluations based on both cartoon character retrieval and clip synthesis demonstrate the effectiveness of the proposed method for cartoon application. Moreover, additional results of content-based image retrieval on benchmark data suggest the generality of semi-MSL for other applications.