By a News Reporter-Staff News Editor at Education Letter -- Investigators publish new report on Neural Networks and Learning Systems. According to news reporting out of Wollongong, Australia, by VerticalNews editors, research stated, "Distance metric learning is of fundamental interest in machine learning because the employed distance metric can significantly affect the performance of many learning methods. Quadratic Mahalanobis metric learning is a popular approach to the problem, but typically requires solving a semidefinite programming (SDP) problem, which is computationally expensive."
Our news journalists obtained a quote from the research from the University of Wollongong, "The worst case complexity of solving an SDP problem involving a matrix variable of size D x D with O(D) linear constraints is about O(D-6.5) using interior-point methods, where D is the dimension of the input data. Thus, the interior-point methods only practically solve problems exhibiting less than a few thousand variables. Because the number of variables is D(D + 1)/2, this implies a limit upon the size of problem that can practically be solved around a few hundred dimensions. The complexity of the popular quadratic Mahalanobis metric learning approach thus limits the size of problem to which metric learning can be applied. Here, we propose a significantly more efficient and scalable approach to the metric learning problem based on the Lagrange dual formulation of the problem. The proposed formulation is much simpler to implement, and therefore allows much larger Mahalanobis metric learning problems to be solved. The time complexity of the proposed method is roughly O(D-3), which is significantly lower than that of the SDP approach. Experiments on a variety of data sets demonstrate that the proposed method achieves an accuracy comparable with the state of the art, but is applicable to significantly larger problems."
According to the news editors, the research concluded: "We also show that the proposed method can be applied to solve more general Frobenius norm regularized SDP problems approximately."
For more information on this research see: Efficient Dual Approach to Distance Metric Learning. IEEE Transactions on Neural Networks and Learning Systems, 2014;25(2):394-406. IEEE Transactions on Neural Networks and Learning Systems can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - www.ieee.org/; IEEE Transactions on Neural Networks and Learning Systems - ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=72)
Our news journalists report that additional information may be obtained by contacting C.H. Shen, University of Wollongong, Sch Comp Sci & Software Engn, Wollongong, NSW 2522, Australia. Additional authors for this research include J. Kim, F.Y. Liu, L. Wang and A. van den Hengel.
Keywords for this news article include: Wollongong, Australia and New Zealand, Neural Networks and Learning Systems
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