Learning a Mahalanobis distance metric for data clustering and classification
نویسندگان
چکیده
Article history: Received 7 October 2007 Received in revised form 27 February 2008 Accepted 16 May 2008
منابع مشابه
Composite Kernel Optimization in Semi-Supervised Metric
Machine-learning solutions to classification, clustering and matching problems critically depend on the adopted metric, which in the past was selected heuristically. In the last decade, it has been demonstrated that an appropriate metric can be learnt from data, resulting in superior performance as compared with traditional metrics. This has recently stimulated a considerable interest in the to...
متن کاملیادگیری نیمه نظارتی کرنل مرکب با استفاده از تکنیک‌های یادگیری معیار فاصله
Distance metric has a key role in many machine learning and computer vision algorithms so that choosing an appropriate distance metric has a direct effect on the performance of such algorithms. Recently, distance metric learning using labeled data or other available supervisory information has become a very active research area in machine learning applications. Studies in this area have shown t...
متن کاملActive Metric Learning for Supervised Classification
Clustering and classification critically rely on distance metrics that provide meaningful comparisons between data points. We present mixedinteger optimization approaches to find optimal distance metrics that generalize the Mahalanobis metric extensively studied in the literature. Additionally, we generalize and improve upon leading methods by removing reliance on pre-designated “target neighbo...
متن کاملSemi-supervised Distributed Clustering with Mahalanobis Distance Metric Learning
Semi-supervised clustering uses a small amount of supervised information to aid unsupervised learning. As one of the semi-supervised clustering methods, metric learning has been widely used to clustering the centralized data points. However, there are many distributed data points, which cannot be centralized for the various reasons. Based on MPCK-MEANS framework [1] , the method of distributed ...
متن کاملMode region detection using improved Competitive Hebbian Learning for unsupervised clustering
The goal of this paper is to propose an improved competitive Hebbian learning for mode detection using a new activation function, to overcome its sensitivity to local irregularities in pattern distribution. This method is involved with an unsupervised clustering approach divided into four processing stages. It begins by the estimation of the probability density function, followed by a competiti...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
- Pattern Recognition
دوره 41 شماره
صفحات -
تاریخ انتشار 2008