Localized Multiple Kernel Learning Research Articles

A radius-incorporated localized multiple kernel learning algorithm for detecting depression in speech

A radius-incorporated localized multiple kernel learning algorithm for detecting depression in speech

Localized multiple kernel learning using graph modularity

• A graph-based heuristic approach for multiple kernel learning (MKL). • Assigns sample-specific kernel weights based on contribution to graph modularity . • Classification performance comparable to state-of-the-art MKL algorithms. • Outperforms other heuristic approaches and has low time complexity. Multiple kernel learning (MKL) algorithms exploit information from multiple feature representations by assigning weights to each representation in the kernel space, and later combining them. However, this ignores the fact that data points exhibit locally varying characteristics. To address this problem, localized MKL algorithms learn locality-specific kernel weights which determine each base kernel’s influence in the locality under consideration. Here, we relate the problem of determining the relevance of base kernels for classification to that of quantifying community structure in graphs. Next, we derive sample-specific kernel weights using graph modularity. Through experiments on publicly available datasets, we show that the proposed approach offers a viable alternative to state-of-the-art MKL approaches while being computationally inexpensive.

Deep Multiple Kernel Learning for Prediction of MicroRNA Precursors

MicroRNAs are a group of noncoding RNAs that are about 20–24 nucleotides in length. They are involved in the physiological processes of many diseases and regulate transcriptional and post-transcriptional gene expression. Therefore, the prediction of microRNAs is of great significance for basic biological research and disease treatment. MicroRNA precursors are the necessary stage of microRNA formation. RBF kernel support vector machines (RBF-SVMs) and shallow multiple kernel support vector machines (MK-SVMs) are often used in microRNA precursors prediction. However, the RBF-SVMs could not represent the richer sample features, and the MK-SVMs just use a simply convex combination of few base kernels. This paper proposed a localized multiple kernel learning model with a nonlinear synthetic kernel (LMKL-D). The nonlinear synthetic kernel was trained by a three-layer deep multiple kernel learning model. The LMKL-D model was tested on 2241 pre-microRNAs and 8494 pseudo hairpin sequences. The experiments showed that the LMKL-D model achieved 93.06% sensitivity, 99.27% specificity, and 98.03% accuracy on the test set. The results showed that the LMKL-D model can increase the complexity of kernels and better predict microRNA precursors. Our LMKL-D model can better predict microRNA precursors compared with the existing methods in specificity and accuracy. The LMKL-D model provides a reference for further validation of potential microRNA precursors.

SVRG-MKL: A Fast and Scalable Multiple Kernel Learning Solution for Features Combination in Multi-Class Classification Problems.

In this paper, we present a novel strategy to combine a set of compact descriptors to leverage an associated recognition task. We formulate the problem from a multiple kernel learning (MKL) perspective and solve it following a stochastic variance reduced gradient (SVRG) approach to address its scalability, currently an open issue. MKL models are ideal candidates to jointly learn the optimal combination of features along with its associated predictor. However, they are unable to scale beyond a dozen thousand of samples due to high computational and memory requirements, which severely limits their applicability. We propose SVRG-MKL, an MKL solution with inherent scalability properties that can optimally combine multiple descriptors involving millions of samples. Our solution takes place directly in the primal to avoid Gram matrices computation and memory allocation, whereas the optimization is performed with a proposed algorithm of linear complexity and hence computationally efficient. Our proposition builds upon recent progress in SVRG with the distinction that each kernel is treated differently during optimization, which results in a faster convergence than applying off-the-shelf SVRG into MKL. Extensive experimental validation conducted on several benchmarking data sets confirms a higher accuracy and a significant speedup of our solution. Our technique can be extended to other MKL problems, including visual search and transfer learning, as well as other formulations, such as group-sensitive (GMKL) and localized MKL (LMKL) in convex settings.

Multimodal Framework for Analyzing the Affect of a Group of People

With the advances in multimedia and the world wide web, users upload millions of images and videos everyone on social networking platforms on the Internet. From the perspective of automatic human behavior understanding, it is of interest to analyze and model the affects that are exhibited by groups of people who are participating in social events in these images. However, the analysis of the affect that is expressed by multiple people is challenging due to the varied indoor and outdoor settings. Recently, a few interesting works have investigated face-based group-level emotion recognition (GER). In this paper, we propose a multimodal framework for enhancing the affective analysis ability of GER in challenging environments. Specifically, for encoding a person's information in a group-level image, we first propose an information aggregation method for generating feature descriptions of face, upper body, and scene. Later, we revisit localized multiple kernel learning for fusing face, upper body, and scene information for GER against challenging environments. Intensive experiments are performed on two challenging group-level emotion databases (HAPPEI and GAFF) to investigate the roles of the face, upper body, scene information, and the multimodal framework. Experimental results demonstrate that the multimodal framework achieves promising performance for GER.

A feature fusion based localized multiple kernel learning system for real world image classification

Real-world image classification, which aims to determine the semantic class of un-labeled images, is a challenging task. In this paper, we focus on two challenges of image classification and propose a method to address both of them simultaneously. The first challenge is that representing images by heterogeneous features, such as color, shape and texture, helps to provide better classification accuracy. The second challenge comes from dissimilarities in the visual appearance of images from the same class (intra class variance) and similarities between images from different classes (inter class relationship). In addition to these two challenges, we should note that the feature space of real-world images is highly complex so they cannot be linearly classified. The kernel trick is efficacious to classify them. This paper proposes a feature fusion based multiple kernel learning (MKL) model for image classification. By using multiple kernels extracted from multiple features, we address the first challenge. To provide a solution for the second challenge, we use the idea of a localized MKL by assigning separate local weights to each kernel. We employed spatial pyramid match (SPM) representation of images and computed kernel weights based on Χ2kernel. Experimental results demonstrate that our proposed model has achieved promising results.

On the Impact of Regularization Variation on Localized Multiple Kernel Learning.

This brief analyzes the effects of regularization variations in the localized kernel weights on the hypothesis generated by localized multiple kernel learning (LMKL) algorithms. Recent research on LMKL includes imposing different regularizations on the localized kernel weights and has led to varying formulations and solution strategies. Following the stability analysis theory as presented by Bousquet and Elisseeff, we give stability bounds based on the norm of the variation of localized kernel weights for three LMKL methods cast in the support vector machine classification framework, including vector -norm LMKL, matrix-regularized -norm LMKL, and samplewise -norm LMKL. Further comparison of these bounds helps to qualitatively reveal the performance differences produced by these regularization methods, that is, matrix-regularized LMKL achieves superior performance, followed by vector -norm LMKL and samplewise -norm LMKL. Finally, a set of experimental results on ten benchmark machine learning UCI data sets is reported and shown to empirically support our theoretical analysis.

Improving Localized Multiple Kernel Learning via Radius-Margin Bound

Localized multiple kernel learning (LMKL) is an effective method of multiple kernel learning (MKL). It tries to learn the optimal kernel from a set of predefined basic kernels by directly using the maximum margin principle, which is embodied in support vector machine (SVM). However, LMKL does not consider the radius of minimum enclosing ball (MEB) which actually impacts the error bound of SVM as well as the separating margin. In the paper, we propose an improved version of LMKL, which is named ILMKL. The proposed method explicitly takes into consideration both the margin and the radius and so achieves better performance over its counterpart. Moreover, the proposed method can automatically tune the regularization parameter when learning the optimal kernel. Consequently, it avoids using the time-consuming cross-validation process to choose the parameter. Comprehensive experiments are conducted and the results well demonstrate the effectiveness and efficiency of the proposed method.

Localized Multiple Kernel Learning With Dynamical Clustering and Matrix Regularization.

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features with regard to their discriminative power for each individual sample. However, the learning of numerous local solutions may not scale well even for a moderately sized training set, and the independently learned local models may suffer from overfitting. Hence, in existing local methods, the distributed samples are typically assumed to share the same weights, and various unsupervised clustering methods are applied as preprocessing. In this paper, to enable the learner to discover and benefit from the underlying local coherence and diversity of the samples, we incorporate the clustering procedure into the canonical support vector machine-based LMKL framework. Then, to explore the relatedness among different samples, which has been ignored in a vector -norm analysis, we organize the cluster-specific kernel weights into a matrix and introduce a matrix-based extension of the -norm for constraint enforcement. By casting the joint optimization problem as a problem of alternating optimization, we show how the cluster structure is gradually revealed and how the matrix-regularized kernel weights are obtained. A theoretical analysis of such a regularizer is performed using a Rademacher complexity bound, and complementary empirical experiments on real-world data sets demonstrate the effectiveness of our technique.

Locally adaptive multiple kernel clustering

Conventional multiple kernel learning aims to construct a global combination of multiple kernels in input space. For a data set which has varying local distributions in input space, using a uniform combination of multiple kernels may not always work well. In this paper, we proposed a localized multiple kernel learning method for clustering. Instead of using a uniform combinational kernel over the whole input space, our method associates to each cluster a localized kernel. We assign to each cluster a weight vector for feature selection and combine each weight vector with a Gaussian kernel to form a unique kernel for the corresponding cluster. By optimizing the weight vector and the width parameter of Gaussian kernel jointly for each cluster, each kernel can be localized to match the data distribution of its corresponding cluster. A locally adaptive strategy based on the kernel k-means clustering is used to optimize the kernel for each cluster. We experimentally compared our methods to the kernel k-means clustering, averaged multiple kernel clustering, self-tuning spectral clustering and Variable Bandwidth Mean Shift algorithm. Experimental results demonstrate the effectiveness of our method.

Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization

Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

Biologically inspired task oriented gist model for scene classification

Capturing the scene gist is account for rapid and accurate scene classification in human visual system. This paper presents a biologically inspired task oriented gist model (BT-Gist) that attempts to emulate two important attributes of biological gist: holistic scene centered spatial layout representation and task oriented resolution determination. For the first attribute, we enrich the model of Oliva and Torralba by refining the low-level features in several biological plausible ways, extending the spatial layout to multiple resolution and followed by perceptually meaningful manifold analysis for a set of multi-resolution biologically inspired intrinsic manifold spatial layouts (BMSLs). Since the optimal resolution that best represents the spatial layout varies from task to task, we embody the second attribute as learning the combination of BMSLs of multiple resolution with respect to their optimal discriminative invariance trade-off for the task at hand, and then cast it in the SVM based localized multiple kernel learning (LMKL) framework, by which the kernel of each scene gist is approximated as a local combination of kernels associated to multi-resolution BMSLs. By exploring the task specific category distribution pattern over BMSL, we define the local model as a category distribution sensitive (CDS) kernel, which can accommodate both the diverse individuality of specific BMSL and the universality shared within the whole category space. Via CDS-LMKL, both the optimal resolution for spatial layouts and the final classifier can be efficiently obtained in a joint manner. We evaluate BT-Gist on four natural scene databases and one cluttered indoor scene database with a range of comparison: From different MKL methods, to various biologically inspired models and BoF based computer vision models. CDS-LMKL leads to better results compared to several existing MKL algorithms. Given the two biological attributes that the framework has to follow, BT-Gist, despite its holistic nature, outperforms existing biologically inspired models and BoF based computer vision models in natural scene classification, and competes with the object segmentation based ROI-Gist in cluttered indoor scene classification.

Localized algorithms for multiple kernel learning

Instead of selecting a single kernel, multiple kernel learning (MKL) uses a weighted sum of kernels where the weight of each kernel is optimized during training. Such methods assign the same weight to a kernel over the whole input space, and we discuss localized multiple kernel learning (LMKL) that is composed of a kernel-based learning algorithm and a parametric gating model to assign local weights to kernel functions. These two components are trained in a coupled manner using a two-step alternating optimization algorithm. Empirical results on benchmark classification and regression data sets validate the applicability of our approach. We see that LMKL achieves higher accuracy compared with canonical MKL on classification problems with different feature representations. LMKL can also identify the relevant parts of images using the gating model as a saliency detector in image recognition problems. In regression tasks, LMKL improves the performance significantly or reduces the model complexity by storing significantly fewer support vectors.

Probability-Confidence-Kernel-Based Localized Multiple Kernel Learning With $l_{p}$ Norm

Localized multiple kernel learning (LMKL) is an attractive strategy for combining multiple heterogeneous features in terms of their discriminative power for each individual sample. However, models excessively fitting to a specific sample would obstacle the extension to unseen data, while a more general form is often insufficient for diverse locality characterization. Hence, both learning sample-specific local models for each training datum and extending the learned models to unseen test data should be equally addressed in designing LMKL algorithm. In this paper, for an integrative solution, we propose a probability confidence kernel (PCK), which measures per-sample similarity with respect to probabilistic-prediction-based class attribute: The class attribute similarity complements the spatial-similarity-based base kernels for more reasonable locality characterization, and the predefined form of involved class probability density function facilitates the extension to the whole input space and ensures its statistical meaning. Incorporating PCK into support-vectormachine-based LMKL framework, we propose a new PCK-LMKL with arbitrary l(p)-norm constraint implied in the definition of PCKs, where both the parameters in PCK and the final classifier can be efficiently optimized in a joint manner. Evaluations of PCK-LMKL on both benchmark machine learning data sets (ten University of California Irvine (UCI) data sets) and challenging computer vision data sets (15-scene data set and Caltech-101 data set) have shown to achieve state-of-the-art performances.

Efficient Learning of Sample-Specific Discriminative Features for Scene Classification

Learning the sample-specific discriminative features based on numerous local learning may not scale well to real world scene classification tasks and suffer from the risk of overfitting. Hence we cast it in SVM based localized multiple kernel learning framework, and design a new strategy to alternately optimize the standard SVM solver and the sample-specific kernel weights, by either a linear programming (for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm) or with closed-form solutions (for <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</sub> -norm). Experiments on both natural scene dataset and cluttered indoor scene dataset demonstrate the effectiveness and efficiency of our approach.

Localized Multiple Kernel Learning for Realistic Human Action Recognition in Videos

Realistic human action recognition in videos has been a useful yet challenging task. Video shots of same actions may present huge intra-class variations in terms of visual appearance, kinetic patterns, video shooting, and editing styles. Heterogeneous feature representations of videos pose another challenge on how to effectively handle the redundancy, complementariness and disagreement in these features. This paper proposes a localized multiple kernel learning (L-MKL) algorithm to tackle the issues above. L-MKL integrates the localized classifier ensemble learning and multiple kernel learning in a unified framework to leverage the strengths of both. The basis of L-MKL is to build multiple kernel classifiers on diverse features at subspace localities of heterogeneous representations. L-MKL integrates the discriminability of complementary features locally and enables localized MKL classifiers to deliver better performance in its own region of expertise. Specifically, L-MKL develops a locality gating model to partition the input space of heterogeneous representations to a set of localities of simpler data structure. Each locality then learns its localized optimal combination of Mercer kernels of heterogeneous features. Finally, the gating model coordinates the localized multiple kernel classifiers globally to perform action recognition. Experiments on two datasets show that the proposed approach delivers promising performance.