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.

ℓ q -Norm Sample-Adaptive Multiple Kernel Learning

Abstract: Existing multiple kernel learning (MKL) algorithms indiscriminately apply the same set of kernel combination weights to all samples by pre-specifying a group of base kernels. Sample-adaptive MKL learning (SAMKL) overcomes this limitation by adaptively switching on/off the base kernels with respect to each sample. However, it restricts to solving MKL problems with pre-specified kernels. And, the formulation of existing SAMKL falls to an $\ell _{1}$ -norm MKL which is not flexible. To allow for robust kernel mixtures that generalize well in practical applications, we extend SAMKL to the arbitrary norm and apply it to image classification. In this paper, we formulate a closed-form solution for optimizing the kernel weights based on the equivalence between group-lasso and MKL, and derive an efficient $\ell _{q}$ -norm ( $q\geq 1$ and denoting the $\ell _{q}$ -norm of kernel weights) SAMKL algorithm. The cutting plane method is used to solve this margin maximization problem. Besides, we propose a framework for solving MKL problems in image classification. Experimental results on multiple data sets show the promising performance of the proposed solution compared with other competitive methods.

Multiple Kernel Learning Using Sparse Representation

Abstract: This paper introduces a kernel machine for multiclass discrimination where the scoring function for each class is constructed using a linear combination over a predefined diverse library of kernel functions. The scoring function is built using an expanded set of the kernel library hence increasing the number of degrees of freedom to analyze the information content of each data sample. To choose the smallest set of kernels that best match desirable first-order moment properties of the class-conditional distribution a regularized linear least-squares problem is solved. The proposed multi-kernel machine is then demonstrated and benchmarked against similar techniques which rely on the use of a single kernel using a satellite imagery dataset for the purposes of discriminating among several vegetation and soil types.

Classifying HEp-2 cells in immunofluorescence images using multiple kernel learning

Abstract: Indirect immunofluorescence (IIF) imaging is an important technique for detecting antinuclear antibodies in HEp-2 cells and therefore employed in the diagnosis of autoimmune diseases and other important pathological conditions involving the immune system. Here, HEp-2 cells are categorised into different groups, which allow to make implications about different autoimmune diseases. Traditionally, this categorisation is performed manually by an expert and is hence both subjective and time intensive. In this paper, we present an effective method for classification of HEp-2 cells in which we first extract local binary pattern (LBP) texture features in form of multi-dimensional LBP (MD-LBP) histograms and then employ a multiple kernel learning approach to classification that integrates a multitude of support vector kernels generated by sampling the feature space. We evaluate our algorithm on the ICPR 2012 HEp-2 contest benchmark dataset, and demonstrate that our employed texture features are indeed useful for the differentiation of HEp-2 cells and that our multiple kernel learning based classification approach outperforms single kernel classification schemes. Our algorithm is shown to provide super performance compared to all techniques that were entered in the competition and to rival results obtained by a human expert.

Multi-view Recognition Using Weighted View Selection

Abstract: In this paper, we present an algorithm for multi-view recognition in a distributed camera setting that learns which viewpoints are most discriminative for particular instances of ambiguity. Our method is built on top of 2D recognition algorithms and casts view selection as the problem of optimizing kernel weights in multiple kernel learning. The main contribution is a locality-sensitive meta-training step to learn a disambiguation function to select the relative weighting of available viewpoints needed to classify a 2D input example. Our method outperforms related approaches on benchmark multi-view action recognition data sets.

Robust Text Detection in Natural Scenes Using Text Geometry and Visual Appearance

Abstract: This paper proposes a new two-phase approach to robust text detection by integrating the visual appearance and the geometric reasoning rules In the first phase, geometric rules are used to achieve a higher recall rate Specifically, a robust stroke width transform(RSWT) feature is proposed to better recover the stroke width by additionally considering the cross of two strokes and the continuousness of the letter border In the second phase, a classification scheme based on visual appearance features is used to reject the false alarms while keeping the recall rate To learn a better classifier from multiple visual appearance features, a novel classification method called double soft multiple kernel learning(DS-MKL) is proposed DS-MKL is motivated by a novel kernel margin perspective for multiple kernel learning and can effectively suppress the influence of noisy base kernels Comprehensive experiments on the benchmark ICDAR2005 competition dataset demonstrate the effectiveness of the proposed two-phase text detection approach over the state-of-the-art approaches by a performance gain up to 44% in terms of F-measure