A Shared Subspace for Multiple Metric Learning
نویسندگان
چکیده
Many machine learning and computer vision problems (clustering, classification) make use of a distance. Starting with [20], it has been shown that it is possible to learn a suitably parametrized distance metric. For this project, we propose a new way of learning multiple metrics for the same dataset. We propose a formulation which shares dimensions of a common low-rank space. This metric not only allows us to pool information across categorization tasks, but can be used to understand which common stimulus dimensions are used by the algorithm.
منابع مشابه
Multi-Task Low-Rank Metric Learning Based on Common Subspace
Multi-task learning, referring to the joint training of multiple problems, can usually lead to better performance by exploiting the shared information across all the problems. On the other hand, metric learning, an important research topic, is however often studied in the traditional single task setting. Targeting this problem, in this paper, we propose a novel multi-task metric learning framew...
متن کاملImage alignment via kernelized feature learning
Machine learning is an application of artificial intelligence that is able to automatically learn and improve from experience without being explicitly programmed. The primary assumption for most of the machine learning algorithms is that the training set (source domain) and the test set (target domain) follow from the same probability distribution. However, in most of the real-world application...
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 کاملInstance Specific Metric Subspace Learning: A Bayesian Approach
Instead of using a uniform metric, instance specific distance learning methods assign multiple metrics for different localities, which take data heterogeneity into consideration. Therefore, they may improve the performance of distance based classifiers, e.g., kNN. Existing methods obtain multiple metrics of test data by either transductively assigning metrics for unlabeled instances or designin...
متن کاملShared Subspace Learning for Latent Representation of Multi-View Data
The pervasive existence of multi-view data has made conventional single view data analysis methods to confront with great challenge. To exploit new analysis technique for multi-view data has become one of active topics in the field of machine learning. From the point of shared subspace learning, this paper focuses on capturing the shared latent representation across multi-view by constructing t...
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:
دوره شماره
صفحات -
تاریخ انتشار 2011