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The UFLK method can extract features from digitized paintings. We were able to extract characteristics of art without any prior information about the nature of the features or the stylistic designation of the paintings. The methods herein may provide art researchers with the latest computational techniques for the documentation, interpretation, and forensics of art. The tools could assist the preservation of culturally sensitive works of art for future generations, and provide new insights into works of art and the artists who created them.

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In this paper, a new approach to predicting the structure of a social network without any prior knowledge from the social links is proposed. In absence of links among nodes, we assume there are other information resources associated with the nodes which are called node profiles. The task of link prediction and recommendation from text data is to learn similarities between the nodes and then translate pair-wise similarities into social links. In other words, the process is to convert a similarity matrix into an adjacency matrix. In this paper, an alternative approach is proposed. First, hidden topics of node profiles are learned using Latent Dirichlet Allocation. Then, by mapping node-topic and topic-topic relations, a new structure called semi-bipartite graph is generated which is slightly different from regular bipartite graph. Finally, by applying topological metrics such as Katz and short path scores to the new structure, we are able to rank and recommend relevant links to each node. The proposed technique is applied to several co-authorship networks. While most link prediction methods are low precision solutions, the proposed method performs effectively and offers high precision.