Representative Multiple Kernel Learning for Classification in Hyperspectral Imagery

Multiple kernel learning (MKL) is a promising family of machine learning algorithms using multiple kernel functions for various challenging data mining tasks. Conventional MKL methods often formulate the problem as an optimization task of learning the optimal combinations of both kernels and classifiers, which usually results in some forms of challenging optimization tasks that are often difficult to be solved. Different from the existing MKL methods, in this paper, we investigate a boosting framework of MKL for classification tasks, i.e., we adopt boosting to solve a variant of MKL problem, which avoids solving the complicated optimization tasks. Specifically, we present a novel framework of Multiple kernel boosting (MKBoost), which applies the idea of boosting techniques to learn kernel-based classifiers with multiple kernels for classification problems. Based on the proposed framework, we propose several variants of MKBoost algorithms and extensively examine their empirical performance on a number of benchmark data sets in comparisons to various state-of-the-art MKL algorithms on classification tasks. Experimental results show that the proposed method is more effective and efficient than the existing MKL techniques.

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.

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 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.

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.

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 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.

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.

Kernel methods have been successfully applied to many machine learning problems. Nevertheless, since the performance of kernel methods depends heavily on the type of kernels being used, identifying good kernels among a set of given kernels is important to the success of kernel methods. A straightforward approach to address this problem is cross-validation by training a separate classifier for each kernel and choosing the best kernel classifier out of them. Another approach is Multiple Kernel Learning (MKL), which aims to learn a single kernel classifier from an optimal combination of multiple kernels. However, both approaches suffer from a high computational cost in computing the full kernel matrices and in training, especially when the number of kernels or the number of training examples is very large. In this paper, we tackle this problem by proposing an efficient online kernel selection algorithm. It incrementally learns a weight for each kernel classifier. The weight for each kernel classifier can help us to select a good kernel among a set of given kernels. The proposed approach is efficient in that (i) it is an online approach and therefore avoids computing all the full kernel matrices before training; (ii) it only updates a single kernel classifier each time by a sampling technique and therefore saves time on updating kernel classifiers with poor performance; (iii) it has a theoretically guaranteed performance compared to the best kernel predictor. Empirical studies on image classification tasks demonstrate the effectiveness of the proposed approach for selecting a good kernel among a set of kernels.

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

Hyperspectral image (HSI) classification has become one of the most significant tasks in the field of hyperspectral analysis. However, classifying each pixel in HSI accurately is challenging due to the curse of dimensionality and limited training samples. In this paper, we present an HSI classification architecture called camera spectral response network (CSR-Net), which can learn the optimal camera spectral response (CSR) function for HSI classification problems and effectively reduce the spectral dimensions of HSI. Specifically, we design a convolutional layer to simulate the capturing process of cameras, which learns the optimal CSR function for HSI classification. Then, spectral and spatial features are further extracted by spectral and spatial attention modules. On one hand, the learned CSR can be implemented physically and directly used to capture scenes, which makes the image acquisition process more convenient. On the other hand, compared with ordinary HSIs, we only need images with far fewer bands, without sacrificing the classification precision and avoiding the curse of dimensionality. The experimental results of four popular public hyperspectral datasets show that our method, with only a few image bands, outperforms state-of-the-art HSI classification methods which utilize the full spectral bands of images.

The paradigm shift brought by deep learning in land cover object classification in hyperspectral images (HSIs) is undeniable, particularly in addressing the intricate 3D cube structure inherent in HSI data. Leveraging convolutional neural networks (CNNs), despite their architectural constraints, offers a promising solution for precise spectral data classification. However, challenges persist in object classification in hyperspectral imagery or hyperspectral image classification, including the curse of dimensionality, data redundancy, overfitting, and computational costs. To tackle these hurdles, we introduce the spectrally segmented-enhanced neural network (SENN), a novel model integrating segmentation-based, multi-layer CNNs, SVM classification, and spectrally segmented dimensionality reduction. SENN adeptly integrates spectral–spatial data and is particularly crucial for agricultural land classification. By strategically fusing CNNs and support vector machines (SVMs), SENN enhances class differentiation while mitigating overfitting through dropout and early stopping techniques. Our contributions extend to effective dimensionality reduction, precise CNN-based classification, and enhanced performance via CNN-SVM fusion. SENN harnesses spectral information to surmount challenges in “hyperspectral image classification in hyperspectral imagery”, marking a significant advancement in accuracy and efficiency within this domain.

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.

Hyperspectral imagery (HSI) classification is essential for remote sensing analysis, utilizing various image bands. Convolutional Neural Networks (CNNs) are prevalent in deep learning for visual data processing, with recent applications in HSI classification primarily employing 2D and 3D CNNs. However, 3D CNNs demand significant computational resources due to their complexity. This paper introduces a two-branch spatial-spectral joint convolutional neural network (SSDB) leveraging an attention mechanism for HSI classification. SSDB effectively extracts spectral and spatial information while reducing model complexity, resulting in a lightweight alternative to 3D CNNs. In this comprehensive hyperspectral image (HSI) classification experiments utilizing the Indian Pines, Pavia University, and Salinas Scene datasets, we benchmarked our findings against leading-edge handcrafted and end-to-end deep learning methodologies, demonstrating exceptionally commendable performance with the SSDB approach.

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