1,421 research outputs found

    Adaptive Seeding for Gaussian Mixture Models

    Full text link
    We present new initialization methods for the expectation-maximization algorithm for multivariate Gaussian mixture models. Our methods are adaptions of the well-known KK-means++ initialization and the Gonzalez algorithm. Thereby we aim to close the gap between simple random, e.g. uniform, and complex methods, that crucially depend on the right choice of hyperparameters. Our extensive experiments indicate the usefulness of our methods compared to common techniques and methods, which e.g. apply the original KK-means++ and Gonzalez directly, with respect to artificial as well as real-world data sets.Comment: This is a preprint of a paper that has been accepted for publication in the Proceedings of the 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2016. The final publication is available at link.springer.com (http://link.springer.com/chapter/10.1007/978-3-319-31750-2 24

    A Comparison of Feature-Combination for Example-Based Super Resolution

    Get PDF
    [[abstract]]Super resolution (SR) in computer vision is an important task. In this paper, we compared several common used features in image super resolution of example-based algorithms. To combine features, we develop a cascade framework to both solve the problem of deciding weights among features and to improve computation efficiency. Finally, we modify the framework to have an adaptive threshold such that not only the computation load is much reduced but the modified framework is suitable to any query image as well as various image databases.[[sponsorship]]the 18th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD)[[conferencetype]]國際[[conferencedate]]2014, 5/13 - 5/16[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Tainan, Taiwa

    Exploiting Anti-monotonicity of Multi-label Evaluation Measures for Inducing Multi-label Rules

    Full text link
    Exploiting dependencies between labels is considered to be crucial for multi-label classification. Rules are able to expose label dependencies such as implications, subsumptions or exclusions in a human-comprehensible and interpretable manner. However, the induction of rules with multiple labels in the head is particularly challenging, as the number of label combinations which must be taken into account for each rule grows exponentially with the number of available labels. To overcome this limitation, algorithms for exhaustive rule mining typically use properties such as anti-monotonicity or decomposability in order to prune the search space. In the present paper, we examine whether commonly used multi-label evaluation metrics satisfy these properties and therefore are suited to prune the search space for multi-label heads.Comment: Preprint version. To appear in: Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2018. See http://www.ke.tu-darmstadt.de/bibtex/publications/show/3074 for further information. arXiv admin note: text overlap with arXiv:1812.0005

    Relation Embedding for Personalised POI Recommendation

    Full text link
    Point-of-Interest (POI) recommendation is one of the most important location-based services helping people discover interesting venues or services. However, the extreme user-POI matrix sparsity and the varying spatio-temporal context pose challenges for POI systems, which affects the quality of POI recommendations. To this end, we propose a translation-based relation embedding for POI recommendation. Our approach encodes the temporal and geographic information, as well as semantic contents effectively in a low-dimensional relation space by using Knowledge Graph Embedding techniques. To further alleviate the issue of user-POI matrix sparsity, a combined matrix factorization framework is built on a user-POI graph to enhance the inference of dynamic personal interests by exploiting the side-information. Experiments on two real-world datasets demonstrate the effectiveness of our proposed model.Comment: 12 pages, 3 figures, Accepted in the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2020

    Taste or Addiction?: Using Play Logs to Infer Song Selection Motivation

    Full text link
    Online music services are increasing in popularity. They enable us to analyze people's music listening behavior based on play logs. Although it is known that people listen to music based on topic (e.g., rock or jazz), we assume that when a user is addicted to an artist, s/he chooses the artist's songs regardless of topic. Based on this assumption, in this paper, we propose a probabilistic model to analyze people's music listening behavior. Our main contributions are three-fold. First, to the best of our knowledge, this is the first study modeling music listening behavior by taking into account the influence of addiction to artists. Second, by using real-world datasets of play logs, we showed the effectiveness of our proposed model. Third, we carried out qualitative experiments and showed that taking addiction into account enables us to analyze music listening behavior from a new viewpoint in terms of how people listen to music according to the time of day, how an artist's songs are listened to by people, etc. We also discuss the possibility of applying the analysis results to applications such as artist similarity computation and song recommendation.Comment: Accepted by The 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017
    corecore