1,421 research outputs found
Adaptive Seeding for Gaussian Mixture Models
We present new initialization methods for the expectation-maximization
algorithm for multivariate Gaussian mixture models. Our methods are adaptions
of the well-known -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 -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
[[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
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
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
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
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