Information overload in structured data

Information overload refers to the difficulty of making decisions caused by too much information. In this dissertation, we address information overload problem in two separate structured domains, namely, graphs and text. Graph kernels have been proposed as an efficient and theoretically sound approach to compute graph similarity. They decompose graphs into certain sub-structures, such as subtrees, or subgraphs. However, existing graph kernels suffer from a few drawbacks. First, the dimension of the feature space associated with the kernel often grows exponentially as the complexity of sub-structures increase. One immediate consequence of this behavior is that small, non-informative, sub-structures occur more frequently and cause information overload. Second, as the number of features increase, we encounter sparsity: only a few informative sub-structures will co-occur in multiple graphs. In the first part of this dissertation, we propose to tackle the above problems by exploiting the dependency relationship among sub-structures. First, we propose a novel framework that learns the latent representations of sub-structures by leveraging recent advancements in deep learning. Second, we propose a general smoothing framework that takes structural similarity into account, inspired by state-of-the-art smoothing techniques used in natural language processing. Both the proposed frameworks are applicable to popular graph kernel families, and achieve significant performance improvements over state-of-the-art graph kernels. In the second part of this dissertation, we tackle information overload in text. We first focus on a popular social news aggregation website, Reddit, and design a submodular recommender system that tailors a personalized frontpage for individual users. Second, we propose a novel submodular framework to summarize videos, where both transcript and comments are available. Third, we demonstrate how to apply filtering techniques to select a small subset of informative features from virtual machine logs in order to predict resource usage

Bag-of-colors for improved image search

International audienceThis paper investigates the use of color information when used within a state-of-the-art large scale image search system. We introduce a simple yet effective and efficient color signature generation procedure. It is used either to produce global or local descriptors. As a global descriptor, it outperforms several state-of-the-art color description methods, in particular the bag-of-words method based on color SIFT. As a local descriptor, our signature is used jointly with SIFT descriptors (no color) to provide complementary information. This significantly improves the recognition rate, outperforming the state of the art on two image search benchmarks. We will provide an open source package of our signature

Textile Memory in Colchane: Weavers Revitalizing the Aymara Tradition

In Aymara culture, textiles have played a fundamental role as highly valued community possessions and significant media for ritual and tradition. In Chilean territory, the Colchane community has been fortunate, because they have here retained, with fidelity and vigor, their culture and traditional textile practices. However, the average age of active weavers is rising and those younger do not have the technical expertise of their elders, which has led to the loss of a significant part of traditional technical knowledge. To not forget the “handwork” became an urgent concern for artisans in the community, members of the Aymar Warmi association, who sought the support of textile professionals to develop a project that would permit them to organize in order to recover the know-how to make some pieces that they had ceased to weave. Between 2015 and 2016, we carried out the project “Memoria Textil: Reproducción y muestra de una selección aymara de Colchane,” (Fondart N  80940) which sought to revitalize local traditional weaving by creating a collection of textile pieces representative of inherited expertise. The collection remained on display in the community as reference materials to be consulted by the weavers. In the project, a methodology was defined in which the professionals assumed the role of facilitators and guides, with all decisions made by the weavers. This led to greater recognition from their peers for local teachers, who directed the transmission of knowledge that went beyond the technical and practical sphere, since together with the textiles their reason for existence was recovered, documenting the contexts in which they were used, some of them forgotten. A consequence of this initiative has been participation of these weavers in the national award Sello Indigena, a prize now received on two occasions based on the practices and pieces recovered

ファッションのための深層学習：服装の統一性評価と格付けおよび推薦

Tohoku University岡谷貴之課