0000000000793410
AUTHOR
Jian Hou
A fast Logdet divergence based metric learning algorithm for large data sets classification
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/463981 Open Access Large data sets classification is widely used in many industrial applications. It is a challenging task to classify large data sets efficiently, accurately, and robustly, as large data sets always contain numerous instances with high dimensional feature space. In order to deal with this problem, in this paper we present an online Logdet divergence based metric learning (LDML) model by making use of the powerfulness of metric learning. We firstly generate a Mahalanobis matrix via learning the training data with LDML model. Mean…
Sampling based average classifier fusion
Published version of an article in the journal: Mathematical Problems in Engineering. Also available from the publisher at: http://dx.doi.org/10.1155/2014/369613 Classifier fusion is used to combine multiple classification decisions and improve classification performance. While various classifier fusion algorithms have been proposed in literature, average fusion is almost always selected as the baseline for comparison. Little is done on exploring the potential of average fusion and proposing a better baseline. In this paper we empirically investigate the behavior of soft labels and classifiers in average fusion. As a result, we find that; by proper sampling of soft labels and classifiers, t…
Research on Vocabulary Sizes and Codebook Universality
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/697245 Open Access Codebook is an effective image representation method. By clustering in local image descriptors, a codebook is shown to be a distinctive image feature and widely applied in object classification. In almost all existing works on codebooks, the building of the visual vocabulary follows a basic routine, that is, extracting local image descriptors and clustering with a user-designated number of clusters. The problem with this routine lies in that building a codebook for each single dataset is not efficient. In order to deal with th…
A novel data-driven fault diagnosis algorithm using multivariate dynamic time warping measure
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/625814 Open Access Process monitoring and fault diagnosis (PM-FD) has been an active research field since it plays important roles in many industrial applications. In this paper, we present a novel data-driven fault diagnosis algorithm which is based on the multivariate dynamic time warping measure. First of all, we propose a Mahalanobis distance based dynamic time warping measure which can compute the similarity of multivariate time series (MTS) efficiently and accurately. Then, a PM-FD framework which consists of data preprocessing, metric lea…
Exploring the best classification from average feature combination
Published version of an article in the journal: Abstract and Applied Analysis. Also available from the publisher at: http://dx.doi.org/10.1155/2014/602763 Open Access Feature combination is a powerful approach to improve object classification performance. While various combination algorithms have been proposed, average combination is almost always selected as the baseline algorithm to be compared with. In previous work we have found that it is better to use only a sample of the most powerful features in average combination than using all. In this paper, we continue this work and further show that the behaviors of features in average combination can be integrated into the k-Nearest-Neighbor …