Effective Improved Graph Transduction

In this paper, we focus on the problem of shape retrieval and clustering. We put two questions together because they are based on the same method, called Improved Graph Transduction. For shape retrieval, we regard the shape as a node in a graph and the similarity of shapes is represented by the edge of the graph.  Then we learn a new distance measure between the query shape and the testing shapes. The main contribution of our work is to merge the most likely node with the query node during the learning process. The appending process helps us to mine the latent information in the propagation. The experimental results on the MPEG-7 data set show that comparing with the existing methods, our method can complete shape retrieval with similar correct rate in less time；For clustering task，the existing literatures in this domain often use the distance measure between the testing data point individual which is proved not enough in the real applications. In this paper, we think about the core concept in semi-supervised learning method, and use a graph to reflect the original distance measure, and combine the density information of the data distribution with the distance measure. Given a set of testing data, we select the original data randomly and use graph transduction iterative on the defined graph. The given algorithm is rapid and steady comparing with the existing clustering method. The experiments show that the novel algorithm is effective for the clustering task.