EasyMKL: a scalable multiple kernel learning algorithm

Aiolli, Fabio; Donini, Michele

doi:10.1016/j.neucom.2014.11.078

Cited by 151 publications

(132 citation statements)

References 7 publications

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“…EasyMKL (Aiolli and Donini 2015) is a recent MKL algorithm able to combine sets of base kernels by solving a simple quadratic problem. Besides its proved empirical effectiveness, a clear advantage of EasyMKL compared to other MKL methods is its high scalability with respect to the number of kernels to be combined.…”

Section: Easymklmentioning

confidence: 99%

“…The problem above is a min-max problem that can be reduced to a simple quadratic problem with a technical derivation described in Aiolli and Donini (2015). Specifically, let γ * be the unique solution of the following quadratic optimization problem:…”

Section: Easymklmentioning

confidence: 99%

“…3 The theoretical analysis of the complexity of EasyMKL, presented in Aiolli and Donini (2015), shows that EasyMKL has a linear increase of the computational complexity with respect to the number of base kernels. In fact, the optimization problem presented in Eq.…”

Section: Analysis Of the Computational Complexitymentioning

confidence: 99%

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Learning deep kernels in the space of dot product polynomials

Donini

Aiolli

2016

Mach Learn

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Recent literature has shown the merits of having deep representations in the context of neural networks. An emerging challenge in kernel learning is the definition of similar deep representations. In this paper, we propose a general methodology to define a hierarchy of base kernels with increasing expressiveness and combine them via multiple kernel learning (MKL) with the aim to generate overall deeper kernels. As a leading example, this methodology is applied to learning the kernel in the space of Dot-Product Polynomials (DPPs), that is a positive combination of homogeneous polynomial kernels (HPKs). We show theoretical properties about the expressiveness of HPKs that make their combination empirically very effective. This can also be seen as learning the coefficients of the Maclaurin expansion of any definite positive dot product kernel thus making our proposed method generally applicable. We empirically show the merits of our approach comparing the effectiveness of the kernel generated by our method against baseline kernels (including homogeneous and non homogeneous polynomials, RBF, etc...) and against another hierarchical approach on several benchmark datasets.

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Section: Easymklmentioning

confidence: 99%

Section: Easymklmentioning

confidence: 99%

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Learning deep kernels in the space of dot product polynomials

Donini

Aiolli

2016

Mach Learn

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“…In addition, the inherent kernel trick of combining linear kernels and nonlinear kernels in MKL makes it more promising in solving fusing information problems. ere is a significant amount of work in the literature for combining multiple kernels [15,16]. Various applications indicate that performance gains can be achieved by linear and nonlinear kernel combinations using MKL methods [17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Multiple Kernel Learning Approach for Air Quality Prediction

Zheng

et al. 2018

Advances in Meteorology

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Air quality prediction is an important research issue due to the increasing impact of air pollution on the urban environment. However, existing methods often fail to forecast high-polluting air conditions, which is precisely what should be highlighted. In this paper, a novel multiple kernel learning (MKL) model that embodies the characteristics of ensemble learning, kernel learning, and representative learning is proposed to forecast the near future air quality (AQ). e centered alignment approach is used for learning kernels, and a boosting approach is used to determine the proper number of kernels. To demonstrate the performance of the proposed MKL model, its performance is compared to that of classical autoregressive integrated moving average (ARIMA) model; widely used parametric models like random forest (RF) and support vector machine (SVM); popular neural network models like multiple layer perceptron (MLP); and long short-term memory neural network. Datasets acquired from a coastal city Hong Kong and an inland city Beijing are used to train and validate all the models. Experiments show that the MKL model outperforms the other models. Moreover, the MKL model has better forecast ability for high health risk category AQ.

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“…They also use some kernel functions that can solve a number of problems, which are not linearly separable. These approaches are time‐consuming (Aiolli & Donini, ), particularly in incremental learning, because not all data are available at the beginning. Research on new kernel is one of the open problems for this challenge (Hooshmand Moghaddam & Hamidzadeh, ).…”

Section: Introductionmentioning

confidence: 99%

Incremental one‐class classification on stationary data stream using two‐quarter sphere

Ghomanjani

Hamidzadeh

2018

Expert Systems

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Data stream is a sequence of data that has unique features. In many data streams, data from one concept is available, and detection of other types of data in data stream is an essential task. One‐class classification is a famous approach to data classification when data from one class is accessible. The principal task of one‐class classification is separating input data in two different parts: target and outlier data. One of the main challenges concerning data classification on the stationary data streams is the response time, which causes negative effects on effective runtime. Most classifiers, that have been proposed on Rd feature space, solve a quadratic problem for classification that leads to extreme runtime increase. In this paper, an incremental one‐class classification on stationary data streams is proposed using two‐quarter sphere (IOCTQ) in order to achieve lower computation cost of classification time, and a linear optimization problem is solved. IOCTQ divides one data classification problem into two data classifiers, and each data point will be individually classified in one‐quarter sphere. The two‐quarter spheres can be run parallel. The results of the experiments have been compared with state‐of‐the‐art methods and show superiority of the IOCTQ method in classification accuracy and time complexity.

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EasyMKL: a scalable multiple kernel learning algorithm

Cited by 151 publications

References 7 publications

Learning deep kernels in the space of dot product polynomials

Learning deep kernels in the space of dot product polynomials

A Multiple Kernel Learning Approach for Air Quality Prediction

Incremental one‐class classification on stationary data stream using two‐quarter sphere

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