Learning Non-Linear Kernel Combinations Subject to General Regularization: Theory and Applications
B. Rakesh Babu
Kernel methods are among the important recent developments in the field of machine learning with applications in computer vision, speech recognition, bio-informatics, etc. This new class of algorithms combine the stability and efficiency of linear algorithms with the descriptive power of nonlinear features. Kernel methods allow data to be mapped (implicitly) to a different space, which is often very high dimensional compared to the input space, so that complex patterns in the data become simpler to detect and learn. Kernel function maps the data implicitly into a different space. Support Vector Machines (SVMs) are one of the kernel methods which is widely successful for classification task. The performance of algorithm depends on the choice of the kernel. Sometimes, finding the right kernel is a complicated task. To overcome this, learning the kernel is the new paradigm which is developed in the recent years. For this, the kernel is parameterized as a weighted linear combination of base kernels. The weights of the kernel are jointly optimized with the objective of the task.
Learning both the SVM parameters and the kernel parameters is a Multiple Kernel Learning (MKL) problem. Many formulations of MKL are presented in literature. However, all these methods restrict to linear combination of base kernels. In this thesis, we show how the existing optimization techniques of MKL formulations can be extended to learn non-linear kernel combinations subject to general regularization on the kernel parameters. Although, this leads to non-convex problem, the proposed method retains all the efficiency of existing large scale optimization algorithms. We name the new MKL formulation as Generalized Multiple Kernel Learning (GMKL). We highlight the advantages of GMKL by tackling problems like feature selection and learning discriminative parts for object categorization problem. Here, we show how the proposed formulation can lead to better results not only as compared to traditional MKL but also as compared to state-of-the-art wrapper and filter methods for feature selection. In the problem of learning discriminative parts for object categorization, our objective is to determine minimal sets of pixels and image regions required for the task. We use the Multiple kernel learning to select the most relevant pixels and regions for classification. We then show how the framework can be used to enhance our understanding of the object categorization problem at hand, determine the importance of context and highlight artifacts in the training data. We also tackle the problem of recognizing characters in images of natural scenes in MKL framework. Traditionally it is not be handled well by OCR techniques. We assess the performance of various features ( using bag-of-visual-words representation ) based on nearest neighbor and SVM classification. Besides this, we investigate the appropriate representation schemes for recognition using MKL.
In short, the contributions of this thesis are: (i) Proposing new MKL formulation that can learn non-linear kernel combinations subject to general regularization on the kernel parameters. (ii) Exploring the utility of multiple kernel learning formulations for feature selection and to the problem of learning informative parts for object category recognition. (iii) Recognition of character images taken in natural scenes using the state of the art object recognition schemes. And also exploring the appropriate representation schemes for recognition using MKL.
|Year of completion:||2010|
|Advisor :||C. V. Jawahar, Dr. Manik Varma|
Manik Varma and Bodla Rakesh Babu - More Generality in Efficient Multiple Kernal Learning Proceedings of International Conference on Machine Learning(ICML 09), 14-18 June, 2009, Montreal, Quebec. [PDF]
- T. E. de Campos and B. R. Babu and M. Varma, Character recognition in natural images,in Proceedings of the International Conference on Computer Vision Theory and Applications, Lisbon, Portugal, February 2009.