MKBoost: A Framework of Multiple Kernel Boosting

Recently, multiple kernel learning (MKL) methods have been developed to improve the flexibility of kernel-based learning machine. The MKL methods generally focus on determining key kernels to be preserved and their significance in optimal kernel combination. Unfortunately, computational demand of finding the optimal combination is prohibitive when the number of training samples and kernels increase rapidly, particularly for hyperspectral remote sensing data. In this paper, we address the MKL for classification in hyperspectral images by extracting the most variation from the space spanned by multiple kernels and propose a representative MKL (RMKL) algorithm. The core idea embedded in the algorithm is to determine the kernels to be preserved and their weights according to statistical significance instead of time-consuming search for optimal kernel combination. The noticeable merits of RMKL consist that it greatly reduces the computational load for searching optimal combination of basis kernels and has no limitation from strict selection of basis kernels like most MKL algorithms do; meanwhile, RMKL keeps excellent properties of MKL in terms of both good classification accuracy and interpretability. Experiments are conducted on different real hyperspectral data, and the corresponding experimental results show that RMKL algorithm provides the best performances to date among several the state-of-the-art algorithms while demonstrating satisfactory computational efficiency.

Multiple kernel learning (MKL) is demonstrated to be flexible and effective in depicting heterogeneous data sources since MKL can introduce multiple kernels rather than a single fixed kernel into applications. However, MKL would get a high time and space complexity in contrast to single kernel learning, which is not expected in real-world applications. Meanwhile, it is known that the kernel mapping ways of MKL generally have two forms including implicit kernel mapping and empirical kernel mapping (EKM), where the latter is less attracted. In this paper, we focus on the MKL with the EKM, and propose a reduced multiple empirical kernel learning machine named RMEKLM for short. To the best of our knowledge, it is the first to reduce both time and space complexity of the MKL with EKM. Different from the existing MKL, the proposed RMEKLM adopts the Gauss Elimination technique to extract a set of feature vectors, which is validated that doing so does not lose much information of the original feature space. Then RMEKLM adopts the extracted feature vectors to span a reduced orthonormal subspace of the feature space, which is visualized in terms of the geometry structure. It can be demonstrated that the spanned subspace is isomorphic to the original feature space, which means that the dot product of two vectors in the original feature space is equal to that of the two corresponding vectors in the generated orthonormal subspace. More importantly, the proposed RMEKLM brings a simpler computation and meanwhile needs a less storage space, especially in the processing of testing. Finally, the experimental results show that RMEKLM owns a much efficient and effective performance in terms of both complexity and classification. The contributions of this paper can be given as follows: (1) by mapping the input space into an orthonormal subspace, the geometry of the generated subspace is visualized; (2) this paper first reduces both the time and space complexity of the EKM-based MKL; (3) this paper adopts the Gauss Elimination, one of the on-the-shelf techniques, to generate a basis of the original feature space, which is stable and efficient.

Kernel Methods have been successfully applied in different tasks and used on a variety of data sample sizes. Multiple Kernel Learning (MKL) and Multilayer Multiple Kernel Learning (MLMKL), as new families of kernel methods, consist of learning the optimal kernel from a set of predefined kernels by using an optimization algorithm. However, learning this optimal combination is considered to be an arduous task. Furthermore, existing algorithms often do not converge to the optimal solution (i.e., weight distribution). They achieve worse results than the simplest method, which is based on the average combination of base kernels, for some real-world applications. In this paper, we present a hybrid model that integrates two methods: Support Vector Machine (SVM) and Multiple Classifier (MC) methods. More precisely, we propose a multiple classifier framework of deep SVMs for classification tasks. We adopt the MC approach to train multiple SVMs based on multiple kernel in a multi-layer structure in order to avoid solving the complicated optimization tasks. Since the average combination of kernels gives high performance, we train multiple models with a predefined combination of kernels. Indeed, we apply a specific distribution of weights for each model. To evaluate the performance of the proposed method, we conducted an extensive set of classification experiments on a number of benchmark data sets. Experimental results show the effectiveness and efficiency of the proposed method as compared to various state-of-the-art MKL and MLMKL algorithms.

Multiple Kernel Learning (MKL) algorithms have recently demonstrated their effectiveness for classifying the data with numerous features. These algorithms aim at learning an optimal composite kernel through combining the basis kernels constructed from different features. Despite their satisfactory results, MKL algorithms assume that the basis kernels are a priori computed. Moreover, they adopt complex optimization methods to train the combination of the basis kernels, which are usually hard to solve and can only handle the binary classification problems. In this paper, a novel MKL framework was introduced in order to address all these issues. This framework optimizes a data-dependent kernel evaluation measure in order to learn both the basis kernels and their combination. The kernel evaluation measure should be able to estimate the goodness of the composite kernel for a multiclass classification problem. In this paper, we defined such a measure based on the similarity between the composite kernel and an ideal kernel. To this end, three different kernel-based similarity measures, namely kernel alignment (KA), centered kernel alignment (CKA), and Hilbert-Schmidt independence criterion (HSIC), were presented. For solving the optimization problem of the proposed MKL framework, we used the metaheuristic optimization algorithms, which in addition to being accurate algorithms can be easily implemented. The performance of the proposed framework was evaluated by classifying the features extracted from two multispectral and hyperspectral datasets. The results showed that this framework outperformed the other state-of-the-art MKL algorithms in terms of both classification accuracy and the computational time.

Kernel methods have become a standard solution for a large number of data analysis, and extensively utilized in the field of signal processing including analysis of speech, image, time series, and DNA sequences. The main difficulty in applying the kernel method is in designing the appropriate kernel for the specific data, and multiple kernel learning (MKL) is one of the principled approaches for kernel design problem. In this paper, a novel multiple kernel learning method based on the notion of Gaussianity evaluated by entropy power inequality is proposed. The notable characteristics of the proposed method are in utilizing the entropy power inequality for kernel learning, and in realizing an MKL algorithm which only optimizes the kernel combination coefficients, while the conventional methods need optimizing both combination coefficients and classifier parameters. The proposed MKL algorithm is experimentally shown to have good classification accuracy.

In this paper, a novel multiple kernel learning (MKL) algorithm is proposed for the classification of hyperspectral images. The proposed MKL algorithm adopts a two-step strategy to learn a multiple kernel machine. In the first step, unsupervised learning is carried out to learn a combined kernel from the predefined base kernels. In our algorithms, low-rank nonnegative matrix factorization (NMF) is used to carry out the unsupervised learning and learn an optimal combined kernel. Furthermore, the kernel NMF (KNMF) is introduced to substitute NMF for enhancing the ability of the unsupervised learning with the predefined base kernels. In the second step, the optimal kernel is embedded into the standard optimization routine of support vector machine (SVM). In addition, we address a major challenge in hyperspectral data classification, i.e., using very few labeled samples in a high-dimensional space. Experiments are conducted on three real hyperspectral datasets, and the experimental results show that the proposed algorithms, especially for KNMF-based MKL, achieve the outstanding performance for hyperspectral image classification with few labeled samples when compared with several state-of-the-art algorithms.

Kernel methods have been successfully used in many practical machine learning problems. Choosing a suitable kernel is left to the practitioner. A common way to an automatic selection of optimal kernels is to learn a linear combination of element kernels. In this paper, a novel framework of multiple kernel learning is proposed based on conditional entropy minimization criterion. For the proposed framework, three multiple kernel learning algorithms are derived. The algorithms are experimentally shown to be comparable to or outperform kernel Fisher discriminant analysis and other multiple kernel learning algorithms on benchmark data sets.

Automatic action recognition in videos is a challenging computer vision task that has become an active research area in recent years. Existing strategies usually use kernel-based learning algorithms that considers a simple combination of different features completely disregarding how such features should be integrated to fit the given problem. Since a given feature is most suitable to describe a given image/video property, the adaptive weighting of such features can improve the performance of the learning algorithm. In this paper, we investigated the use of the Multiple Kernel Learning (MKL) algorithm to adaptive search for the best linear relation among the considered features. MKL is an extension of the support vector machines (SVMs) to work with a weighted linear combination of several single kernels. This approach allows to simultaneously estimate the weights for the multiple kernels combination as well as the underlying SVM parameters. In order to prove the validity of the MKL approach, we considered a descriptor composed of multiple features aligned with dense trajectories. We experimented our approach on a database containing 36 cooking actions. Results confirm that the use of MKL improves the classification performance.

A nonlinear subspace multiple kernel learning for financial distress prediction of Chinese listed companies

A lot of image processing research works focus on natural images, such as in classification, clustering, and the research on the recognition of artworks (such as oil paintings), from feature extraction to classifier design, is relatively few. This paper focuses on oil painter recognition and tries to find the mobile application to recognize the painter. This paper proposes a cluster multiple kernel learning algorithm, which extracts oil painting features from three aspects: color, texture, and spatial layout, and generates multiple candidate kernels with different kernel functions. With the results of clustering numerous candidate kernels, we selected the sub-kernels with better classification performance, and use the traditional multiple kernel learning algorithm to carry out the multi-feature fusion classification. The algorithm achieves a better result on the Painting91 than using traditional multiple kernel learning directly.

Identifying sandstone images and judging the types of minerals play an important role in oil and gas reservoir exploration and evaluation. Multiple kernel learning (MKL) method has shown high performance in solving some practical applications. While this method belongs to a shallow structure and cannot handle relatively complex problems well. With the development of deep learning in recent years, many researchers have proposed a deep multiple layer multiple kernel learning (DMLMKL) method based on deep structure. While the existing DMLMKL method only considers the deep representation of the data but ignores the shallow representation between the data. Therefore, this paper propose a multiple scale multiple layer multiple kernel learning (MS-DKL) method that “richer” feature data by fusing deep and shallow representations of mineral image features. Mineral recognition results show that MS-DKL algorithm is higher accuracy in mineral recognition than the MKL and DMLMKL methods.KeywordsMineral recognitionDeep kernel learningMultiple scaleSLIC

As a rapid developing research topic in the machine vision field, image-based hand posture recognition has the potential to be an efficient and intuitive tool of human-computer interaction. For improving the accuracy of multi-class hand postures and extending the algorithm generalization,a novel hand posture recognition method is proposed by integrating the multiple image features and multiple kernels learning support vector machine(SVM). Firstly, three types of feature descriptors are extracted to describe the characteristics of a hand posture image. Shape context descriptor represents distribution characteristics of the edge points of the hand posture image. Pyramid histogram of oriented gradient describes characteristics of local and global shape effectively. The Bag of Feature(BOF) algorithm describes the surface texture characteristics of the posture image. Secondly, the Chamfer kernel and histogram intersection kernel are rebuilt to obtain the basis kernels of the features. And the combined kernel is constructed by weighting the basis kernels.So the heterogeneous features fusion realizes. Finally, the classification model and optimal fusion weights are calculated by using multiple kernels learning algorithm. The unknown category posture can be recognized by the trained multiple kernels of SVM. Experiments on Jochen Triesch's hand posture dataset demonstrate that the proposed method obtains higher recognition rate than the traditional single-kernel classifier and other recent methods.

Elastic Multiple Kernel Learning

Multiple Kernel Based Transfer Learning for the Few-Shot Recognition Task in Smart Home Scene

In classification tasks, labeled data is a necessity but sometimes difficult or expensive to obtain. On the contrary, unlabeled data is usually abundant. Recently, different active learning algorithms are proposed to alleviate this issue by selecting the most informative data points to label. One family of active learning methods comes from Optimum Experimental Design (OED) in statistics. Instead of selecting data points one by one iteratively, OED-based approaches select data in a one-shot manner, that is, a fixed-sized subset is selected from the unlabeled dataset for manually labeling. These methods usually use kernels to represent pair-wise similarities between different data points. It is well known that choosing optimal kernel types (e.g. Gaussian kernel) and kernel parameters (e.g. kernel width) is tricky, and a common way to resolve it is by Multiple Kernel Learning (MKL), i.e., to construct a few candidate kernels and merge them to form a consensus kernel. There would be different ways to combine multiple kernels, one of which, called the the globalised approach is to assign a weight to each candidate kernel. In practice different data points in the same candidate kernel may not have the same contribution in the consensus kernel, this requires assigning different weights to different data points in the same candidate kernel, leading to the localized approach. In this paper, we introduce MKL to OED-based active learning, specifically we propose globalised and localized multiple kernel active learning methods, respectively. Our experiments on six benchmark datasets demonstrate that the proposed methods have better performance than existing OED-based active learning methods.