Multiple kernel learning

About: Multiple kernel learning is a research topic. Over the lifetime, 1630 publications have been published within this topic receiving 56082 citations.

Learning with Multiple Similarities

Abstract: Title of dissertation: LEARNINGWITHMULTIPLE SIMILARITIES Abhishek Kumar, Doctor of Philosophy, 2013 Dissertation directed by: Professor Hal Daume III Department of Computer Science The notion of similarities between data points is central to many classification and clustering algorithms. We often encounter situations when there are more than one set of pairwise similarity graphs between objects, either arising from different measures of similarity between objects or from a single similarity measure defined on multiple data representations, or a combination of these. Such examples can be found in various applications in computer vision, natural language processing and computational biology. Combining information from these multiple sources is often beneficial in learning meaningful concepts from data. This dissertation proposes novel methods to effectively fuse information from these multiple similarity graphs, targeted towards two fundamental tasks in machine learning classification and clustering. In particular, I propose two models for learning spectral embedding from multiple similarity graphs using ideas from co-training and co-regularization. Further, I propose a novel approach to the problem of multiple kernel learning (MKL), converting it to a more familiar problem of binary classification in a transformed space. The proposed MKL approach learns a “good” linear combination of base kernels by optimizing a quality criterion that is justified both empirically and theoretically. The ideas of the proposed MKL method are also extended to learning nonlinear combinations of kernels, in particular, polynomial kernel combination and more general nonlinear kernel combination using random forests. Learning with Multiple Similarities

People Re-identification Based on Online Multiple Kernel Learning in Video Surveillance

Abstract: In the non-overlapping multi-camera or single camera video surveillance,re-identification of tracked target is very important.Due to weakness of traditional support vector machine in feature fusion,a new people re-identification method is proposed based on online multiple kernel learning.We extract complementary visual word tree histogram and global color histogram from tracked people foreground image sequence in video,and then multiple kernel learning method is used for online train people visual appearance.Finally,we obtain multiple kernel feature fusion model of people appearance.Experimental results show that our method can train people appearance model rapidly,meet the real-time requirement of video surveillance,and attain higher recognition performance than single feature appearance model and single kernel support vector machine method.

Geotagged iage rcognition by cmbining tree dfferent knds of golocation fatures

Abstract: Scenes and objects represented in photos have causal relationship to the places where they are taken. In this paper, we propose using geo-information such as aerial photos and location-related texts as features for geotagged image recognition and fusing them with Multiple Kernel Learning (MKL). By the experiments, we have verified the possibility for reflecting location contexts in image recognition by evaluating not only recognition rates, but feature fusion weights estimated by MKL. As a result, the mean average precision (MAP) for 28 categories increased up to 80.87% by the proposed method, compared with 77.71% by the baseline. Especially, for the categories related to location-dependent concepts, MAP was improved by 6.57 points.