Reduced-Rank Local Distance Metric Learning
نویسندگان
چکیده
Y. Huang acknowledges partial support from a UCF Graduate College Presidential Fellowship and National Science Foundation (NS F) grant No. 1200566. C. Li acknowledges partial support from NSF grants No. 0806931 and No. 0963146. M. Georgiopoulos acknowledges partial support from NSF grants No. 1161228 and No. 0525429. G. G. Anagnostopoulos acknowledges partial support from NSF grant No. 1263011. MACHINE LEARNING LABORATORY @ UCF
منابع مشابه
An Effective Approach for Robust Metric Learning in the Presence of Label Noise
Many algorithms in machine learning, pattern recognition, and data mining are based on a similarity/distance measure. For example, the kNN classifier and clustering algorithms such as k-means require a similarity/distance function. Also, in Content-Based Information Retrieval (CBIR) systems, we need to rank the retrieved objects based on the similarity to the query. As generic measures such as ...
متن کاملSemi-supervised Distance Metric Learning in High-Dimensional Spaces by Using Equivalence Constraints
This paper introduces a semi-supervised distance metric learning algorithm which uses pairwise equivalence (similarity and dissimilarity) constraints to discover the desired groups within high-dimensional data. In contrast to the traditional full rank distance metric learning algorithms, the proposed method can learn nonsquare projection matrices that yield low rank distance metrics. This bring...
متن کاملSemi-supervised Distance Metric Learning for Visual Object Classification
This paper describes a semi-supervised distance metric learning algorithm which uses pairwise equivalence (similarity and dissimilarity) constraints to discover the desired groups within high-dimensional data. As opposed to the traditional full rank distance metric learning algorithms, the proposed method can learn nonsquare projection matrices that yield low rank distance metrics. This brings ...
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 کاملLocal Distance Metric Learning for Nearest Neighbor Algorithm
Distance metric learning is a successful way to enhance the performance of the nearest neighbor classifier. In most cases, however, the distribution of data does not obey a regular form and may change in different parts of the feature space. Regarding that, this paper proposes a novel local distance metric learning method, namely Local Mahalanobis Distance Learning (LMDL), in order to enhance t...
متن کامل