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.

Automatic plankton image classification combining multiple view features via multiple kernel learning

Abstract: Plankton, including phytoplankton and zooplankton, are the main source of food for organisms in the ocean and form the base of marine food chain. As the fundamental components of marine ecosystems, plankton is very sensitive to environment changes, and the study of plankton abundance and distribution is crucial, in order to understand environment changes and protect marine ecosystems. This study was carried out to develop an extensive applicable plankton classification system with high accuracy for the increasing number of various imaging devices. Literature shows that most plankton image classification systems were limited to only one specific imaging device and a relatively narrow taxonomic scope. The real practical system for automatic plankton classification is even non-existent and this study is partly to fill this gap. Inspired by the analysis of literature and development of technology, we focused on the requirements of practical application and proposed an automatic system for plankton image classification combining multiple view features via multiple kernel learning (MKL). For one thing, in order to describe the biomorphic characteristics of plankton more completely and comprehensively, we combined general features with robust features, especially by adding features like Inner-Distance Shape Context for morphological representation. For another, we divided all the features into different types from multiple views and feed them to multiple classifiers instead of only one by combining different kernel matrices computed from different types of features optimally via multiple kernel learning. Moreover, we also applied feature selection method to choose the optimal feature subsets from redundant features for satisfying different datasets from different imaging devices. We implemented our proposed classification system on three different datasets across more than 20 categories from phytoplankton to zooplankton. The experimental results validated that our system outperforms state-of-the-art plankton image classification systems in terms of accuracy and robustness. This study demonstrated automatic plankton image classification system combining multiple view features using multiple kernel learning. The results indicated that multiple view features combined by NLMKL using three kernel functions (linear, polynomial and Gaussian kernel functions) can describe and use information of features better so that achieve a higher classification accuracy.

COFFIN: A Computational Framework for Linear SVMs

Abstract: In a variety of applications, kernel machines such as Support Vector Machines (SVMs) have been used with great success often delivering state-of-the-art results. Using the kernel trick, they work on several domains and even enable heterogeneous data fusion by concatenating feature spaces or multiple kernel learning. Unfortunately, they are not suited for truly large-scale applications since they suffer from the curse of supporting vectors, i.e., the speed of applying SVMs decays linearly with the number of support vectors. In this paper we develop COFFIN — a new training strategy for linear SVMs that effectively allows the use of on demand computed kernel feature spaces and virtual examples in the primal. With linear training and prediction effort this framework leverages SVM applications to truly large-scale problems: As an example, we train SVMs for human splice site recognition involving 50 million examples and sophisticated string kernels. Additionally, we learn an SVM based gender detector on 5 million examples on low-tech hardware and achieve beyond the state-of-the-art accuracies on both tasks. Source code, data sets and scripts are freely available from http://sonnenburgs.de/soeren/coffin.

SPF-GMKL: generalized multiple kernel learning with a million kernels

Abstract: Multiple Kernel Learning (MKL) aims to learn the kernel in an SVM from training data. Many MKL formulations have been proposed and some have proved effective in certain applications. Nevertheless, as MKL is a nascent field, many more formulations need to be developed to generalize across domains and meet the challenges of real world applications. However, each MKL formulation typically necessitates the development of a specialized optimization algorithm. The lack of an efficient, general purpose optimizer capable of handling a wide range of formulations presents a significant challenge to those looking to take MKL out of the lab and into the real world.This problem was somewhat alleviated by the development of the Generalized Multiple Kernel Learning (GMKL) formulation which admits fairly general kernel parameterizations and regularizers subject to mild constraints. However, the projected gradient descent GMKL optimizer is inefficient as the computation of the step size and a reasonably accurate objective function value or gradient direction are all expensive. We overcome these limitations by developing a Spectral Projected Gradient (SPG) descent optimizer which: a) takes into account second order information in selecting step sizes; b) employs a non-monotone step size selection criterion requiring fewer function evaluations; c) is robust to gradient noise, and d) can take quick steps when far away from the optimum.We show that our proposed SPG-GMKL optimizer can be an order of magnitude faster than projected gradient descent on even small and medium sized datasets. In some cases, SPG-GMKL can even outperform state-of-the-art specialized optimization algorithms developed for a single MKL formulation. Furthermore, we demonstrate that SPG-GMKL can scale well beyond gradient descent to large problems involving a million kernels or half a million data points. Our code and implementation are available publically.