Analyzing Local Structure in Kernel-Based Learning: Explanation, Complexity, and Reliability Assessment

Montavon, Grégoire; Braun, Mikio L.; Krueger, Tammo; Müller, Klaus‐Robert

doi:10.1109/msp.2013.2249294

Cited by 39 publications

(24 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Indeed, one only needs to replace the inner product operator of a linear technique with an appropriate kernel function k (i.e., a positive semi-definite symmetric function), which arises as a similarity measure that can be thought as an inner product between pairs of data in the feature space. Here, the original nonlinear problem can be transformed into a linear formulation in a higher dimensional space ℱ with an appropriate kernel k [65]:…”

Section: Transfer Learningmentioning

confidence: 99%

“…Moreover, for a specific problem, the selection of the best kernel function is still an open issue, although ample experimental evidence in the literature supports that the popular kernel functions such as Gaussian kernels and polynomial kernels perform well in most cases. At the root of the success of kernel-based learning, the combination of high expressive power with the possibility to perform the numerous analyses has been developed in many challenging applications [65], e.g., online classification [66], convexly constrained parameter/function estimation [67], beamforming problems [68], and adaptive multiregression [69]. One of the most popular surveys about introducing kernel-based learning algorithms is [70], in which an introduction of the exciting field of kernel-based learning methods and applications was given.…”

Section: Transfer Learningmentioning

confidence: 99%

See 1 more Smart Citation

A survey of machine learning for big data processing

Qiu

Ding

et al. 2016

EURASIP J. Adv. Signal Process.

624

361

View full text Add to dashboard Cite

There is no doubt that big data are now rapidly expanding in all science and engineering domains. While the potential of these massive data is undoubtedly significant, fully making sense of them requires new ways of thinking and novel learning techniques to address the various challenges. In this paper, we present a literature survey of the latest advances in researches on machine learning for big data processing. First, we review the machine learning techniques and highlight some promising learning methods in recent studies, such as representation learning, deep learning, distributed and parallel learning, transfer learning, active learning, and kernel-based learning. Next, we focus on the analysis and discussions about the challenges and possible solutions of machine learning for big data. Following that, we investigate the close connections of machine learning with signal processing techniques for big data processing. Finally, we outline several open issues and research trends.

show abstract

Section: Transfer Learningmentioning

confidence: 99%

Section: Transfer Learningmentioning

confidence: 99%

A survey of machine learning for big data processing

Qiu

Ding

et al. 2016

EURASIP J. Adv. Signal Process.

624

361

View full text Add to dashboard Cite

show abstract

“…The accuracy of predictions depends strongly on how crystals are represented. We found that Coulomb matrices, while being successful for predicting properties in molecules [25,26], are not suitable to describe crystal structures well enough. Instead, we have proposed a representation inspired by partial radial distribution functions which is invariant with respect to translation, rotation and the choice of the unit cell.…”

Section: Arxiv:13071266v3 [Cond-matmtrl-sci] 22 May 2014mentioning

confidence: 92%

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

et al. 2014

View full text Add to dashboard Cite

High-throughput density-functional calculations of solids are highly time consuming. As an alternative, we propose a machine learning approach for the fast prediction of solid-state properties. To achieve this, LSDA calculations are used as training set. We focus on predicting the value of the density of electronic states at the Fermi energy. We find that conventional representations of the input data, such as the Coulomb matrix, are not suitable for the training of learning machines in the case of periodic solids. We propose a novel crystal structure representation for which learning and competitive prediction accuracies become possible within an unrestricted class of spd systems of arbitrary unit-cell size.In recent years ab-initio high-throughput computational methods (HTM) have proven to be a powerful and successful tool to predict new materials and to optimize desired materials properties. Phase diagrams of multicomponent crystals [1][2][3] and alloys [4] have been successfully predicted. High-impact technological applications have been achieved by improving the performance of Lithium based batteries [5][6][7], by tailoring the nonlinear optical response in organic molecules [8] for optical signal processing, by designing desired current-voltage characteristics [9] for photovoltaic materials, by optimizing the electrode transparency and conductivity [10] for solar cell technology, and by screening metals for the highest amalgamation enthalpy [11] to efficiently remove Hg pollutants in coal gasification.However, the computational cost of electronic structure calculations poses a serious bottleneck for HTM. Thinking of quaternary, quinternary, etc., compounds, the space of possible materials becomes so large, and the complexity of the unit cells so high that, even within efficient Kohn-Sham density functional theory (KS-DFT), a systematic high-throughput exploration grows beyond reach for present-day computing facilities. As a way out, one would like to have a more direct way to access the physical property of interest without actually solving the KS-DFT equations. Machine learning (ML) techniques offer an attractive possibility of this type. ML-based calculations are very fast, typically requiring only fractions of a second to predict a specific property of a given material, after having trained the ML model on a representative training set of materials.ML methods rely on two main ingredients, the learning algorithm itself and the representation of the input data. There are many different ways of representing a given material or compound. While, from the physicist's point of view, the information is simply given by the charges and the positions of the nuclei, for ML algorithms the specific mathematical form in which this information is given to the machine, is crucial. Roughly speaking, ML algorithms assume a nonlinear map between input data (representing the materials or compounds in our case) and the material-specific property to be predicted. Whether or not a machine can approximate the unknown nonlinear ...

show abstract

“…It could be seen that the scatters was discontinuous, that was because the step of FVC was set as 0.05 when the training dataset was generated using PROSAIL model. To analyze models' robustness to local consistency and reliability of the estimated error [62,63], Figure 6 exhibits the evolution of the RMSE as a function of the number of predictions. The shapes of the two curves in Figure 6 are similar for two methods, which shows a consistent better performance of MARS over BPNNs.…”

Section: Accuracy Assessment Over the Simulated Datasetmentioning

confidence: 99%

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

et al. 2017

View full text Add to dashboard Cite

Abstract:Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R 2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R 2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R 2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data.

show abstract

Analyzing Local Structure in Kernel-Based Learning: Explanation, Complexity, and Reliability Assessment

Cited by 39 publications

References 28 publications

A survey of machine learning for big data processing

A survey of machine learning for big data processing

How to represent crystal structures for machine learning: Towards fast prediction of electronic properties

A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data

Contact Info

Product

Resources

About