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53
Kernelbased methods for hyperspectral image classification
 IEEE Transactions on Geoscience and Remote Sensing
, 2005
"... Abstract—This paper presents the framework of kernelbased methods in the context of hyperspectral image classification, illustrating from a general viewpoint the main characteristics of different kernelbased approaches and analyzing their properties in the hyperspectral domain. In particular, we a ..."
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Cited by 149 (24 self)
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Abstract—This paper presents the framework of kernelbased methods in the context of hyperspectral image classification, illustrating from a general viewpoint the main characteristics of different kernelbased approaches and analyzing their properties in the hyperspectral domain. In particular, we assess performance of regularized radial basis function neural networks (RegRBFNN), standard support vector machines (SVMs), kernel Fisher discriminant (KFD) analysis, and regularized AdaBoost (RegAB). The novelty of this work consists in: 1) introducing RegRBFNN and RegAB for hyperspectral image classification; 2) comparing kernelbased methods by taking into account the peculiarities of hyperspectral images; and 3) clarifying their theoretical relationships. To these purposes, we focus on the accuracy of methods when working in noisy environments, high input dimension, and limited training sets. In addition, some other important issues are discussed, such as the sparsity of the solutions, the computational burden, and the capability of the methods to provide outputs that can be directly interpreted as probabilities. Index Terms—AdaBoost, feature space, hyperspectral classification, kernelbased methods, kernel Fisher discriminant analysis, radial basis function neural networks, regularization, support vector machines. I.
SpatioSpectral Filters for Improving the Classification of Single Trial EEG
 IEEE Trans. Biomed. Eng
, 2005
"... Data recorded in EEG based BrainComputer Interface experiments is generally very noisy, nonstationary and contaminated with artifacts, that can deteriorate discrimination/classification methods. In this work we extend the Common Spatial Pattern (CSP) algorithm with the aim to alleviate these adver ..."
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Cited by 72 (14 self)
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Data recorded in EEG based BrainComputer Interface experiments is generally very noisy, nonstationary and contaminated with artifacts, that can deteriorate discrimination/classification methods. In this work we extend the Common Spatial Pattern (CSP) algorithm with the aim to alleviate these adverse effects. In particular we suggest an extension of CSP to the state space, which utilizes the method of time delay embedding. As we will show, this allows for individually tuned frequency filters at each electrode position and thus yields an improved and more robust machine learning procedure. The advantages of the proposed method over the original CSP method are verified in terms of an improved information transfer rate (bits per trial) on a set of EEGrecordings from experiments of imagined limb movements.
A Mathematical Programming Approach to the Kernel Fisher Algorithm
, 2001
"... We investigate a new kernelbased classifier: the Kernel Fisher Discriminant (KFD). A mathematical programming formulation based on the observation that KFD maximizes the average margin permits an interesting modification of the original KFD algorithm yielding the sparse KFD. We find that both, KFD ..."
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Cited by 70 (14 self)
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We investigate a new kernelbased classifier: the Kernel Fisher Discriminant (KFD). A mathematical programming formulation based on the observation that KFD maximizes the average margin permits an interesting modification of the original KFD algorithm yielding the sparse KFD. We find that both, KFD and the proposed sparse KFD, can be understood in an unifying probabilistic context. Furthermore, we show connections to Support Vector Machines and Relevance Vector Machines. From this understanding, we are able to outline an interesting kernelregression technique based upon the KFD algorithm. Simulations support the usefulness of our approach.
Nonparametric Weighted Feature Extraction for Classification
 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
, 2004
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An Improved Training Algorithm for Kernel Fisher Discriminants
 In Proceedings AISTATS 2001
, 2001
"... We present a fast training algorithm for the kernel Fisher discriminant classifier. It uses a greedy approximation technique and has an empirical scaling behavior which improves upon the state of the art by more than an order of magnitude, thus rendering the kernel Fisher algorithm a viable option a ..."
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Cited by 40 (3 self)
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We present a fast training algorithm for the kernel Fisher discriminant classifier. It uses a greedy approximation technique and has an empirical scaling behavior which improves upon the state of the art by more than an order of magnitude, thus rendering the kernel Fisher algorithm a viable option also for large datasets. 1
Invariant common spatial patterns: Alleviating nonstationarities in braincomputer interfacing
 In Ad. in NIPS 20
, 2008
"... BrainComputer Interfaces can suffer from a large variance of the subject conditions within and across sessions. For example vigilance fluctuations in the individual, variable task involvement, workload etc. alter the characteristics of EEG signals and thus challenge a stable BCI operation. In the p ..."
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Cited by 30 (9 self)
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BrainComputer Interfaces can suffer from a large variance of the subject conditions within and across sessions. For example vigilance fluctuations in the individual, variable task involvement, workload etc. alter the characteristics of EEG signals and thus challenge a stable BCI operation. In the present work we aim to define features based on a variant of the common spatial patterns (CSP) algorithm that are constructed invariant with respect to such nonstationarities. We enforce invariance properties by adding terms to the denominator of a Rayleigh coefficient representation of CSP such as disturbance covariance matrices from fluctuations in visual processing. In this manner physiological prior knowledge can be used to shape the classification engine for BCI. As a proof of concept we present a BCI classifier that is robust to changes in the level of parietal αactivity. In other words, the EEG decoding still works when there are lapses in vigilance. 1
Fall detection by embedding an accelerometer in cellphone and using kfd algorithm
 IJCSNS International Journal of Computer Science and Network Security
, 2006
"... The fall is a risky event in the elderly people’s daily living, especially the independent living, it often cause serious injury both in physiology and psychology. Wearable sensor based fall detection system had been proved in many experiments for its feasibility and effectiveness, but there remain ..."
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Cited by 23 (0 self)
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The fall is a risky event in the elderly people’s daily living, especially the independent living, it often cause serious injury both in physiology and psychology. Wearable sensor based fall detection system had been proved in many experiments for its feasibility and effectiveness, but there remain some crucial problems, include: the people maybe forget to wear the clothes with micro sensors, which device standard should be selected between medical device standard and mass market standard, and how to control the false alarm probability to fit the individualized requirements. To deal with these problems, we think it is a reasonable design to combine micro sensors with an ambulatory daily using device which has a common network interface, and adjust the classification parameters via a remote server. In this paper, we embed a triaxial accelerometer in a cellphone, connect to Internet via the wireless channel, and using 1Class SVM (Support Vector Machine) algorithm for the preprocessing, KFD (Kernel Fisher Discriminant) and kNN (Nearest Neighbour) algorithm for the precise classification. And there were 32 volunteers, 12 elders (age 6080) and 20 younger (age 2039), attended our experiments, the results show that this method can detect the falls effectively and make less disturbance to people’s daily living than the general wearable sensor based fall detection systems. Key words: Accelerometer, Cellphone, Fall detection,1Class SVM, KFD.
Information bottleneck for gaussian variables
 in Advances in Neural Information Processing Systems 16
, 2003
"... ∗ Both authors contributed equally The problem of extracting the relevant aspects of data was addressed through the information bottleneck (IB) method, by (soft) clustering one variable while preserving information about another relevance variable. An interesting question addressed in the current ..."
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Cited by 21 (3 self)
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∗ Both authors contributed equally The problem of extracting the relevant aspects of data was addressed through the information bottleneck (IB) method, by (soft) clustering one variable while preserving information about another relevance variable. An interesting question addressed in the current work is the extension of these ideas to obtain continuous representations (embeddings) that preserve relevant information, rather than discrete clusters. We give a formal definition of the general continuous IB problem and obtain an analytic solution for the optimal representation for the important case of multivariate Gaussian variables. The obtained optimal representation is a noisy linear projection to eigenvectors of the normalized correlation matrix Σ xyΣ −1 x, which is also the basis obtained in Canonical Correlation Analysis. However, in Gaussian IB, the compression tradeoff parameter uniquely determines the dimension, as well as the scale of each eigenvector. This introduces a novel interpretation where solutions of different ranks lie on a continuum parametrized by the compression level. Our analysis also provides analytic expression for the optimal tradeoff the information curve in terms of the eigenvalue spectrum. 1
Particle swarm model selection
 JMLR, Special Topic on Model Selection
, 2009
"... This paper proposes the application of particle swarm optimization (PSO) to the problem of full model selection, FMS, for classification tasks. FMS is defined as follows: given a pool of preprocessing methods, feature selection and learning algorithms, to select the combination of these that obtains ..."
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Cited by 18 (7 self)
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This paper proposes the application of particle swarm optimization (PSO) to the problem of full model selection, FMS, for classification tasks. FMS is defined as follows: given a pool of preprocessing methods, feature selection and learning algorithms, to select the combination of these that obtains the lowest classification error for a given data set; the task also includes the selection of hyperparameters for the considered methods. This problem generates a vast search space to be explored, well suited for stochastic optimization techniques. FMS can be applied to any classification domain as it does not require domain knowledge. Different model types and a variety of algorithms can be considered under this formulation. Furthermore, competitive yet simple models can be obtained with FMS. We adopt PSO for the search because of its proven performance in different problems and because of its simplicity, since neither expensive computations nor complicated operations are needed. Interestingly, the way the search is guided allows PSO to avoid overfitting to some extend. Experimental results on benchmark data sets give evidence that the proposed approach is very effective, despite its simplicity. Furthermore, results obtained in the framework of a model selection challenge show the competitiveness of the models selected with PSO, compared to models selected with other techniques that focus on a single algorithm and that use domain knowledge.
Stationary common spatial patterns for braincomputer interfacing
 Journal of Neural Engineering
, 2012
"... Abstract. Classifying motion intentions in BrainComputer Interfacing (BCI) is a demanding task as the recorded EEG signal is not only noisy and has limited spatial resolution but it is also intrinsically nonstationary. The nonstationarities in the signal may come from many different sources, for ..."
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Cited by 16 (8 self)
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Abstract. Classifying motion intentions in BrainComputer Interfacing (BCI) is a demanding task as the recorded EEG signal is not only noisy and has limited spatial resolution but it is also intrinsically nonstationary. The nonstationarities in the signal may come from many different sources, for instance electrode artifacts, muscular activity or changes of task involvement, and often deteriorate classification performance. This is mainly because features extracted by standard methods like Common Spatial Patterns (CSP) are not invariant to variations of the signal properties, thus should also change over time. Although many extensions of CSP were proposed to, for example, reduce the sensitivity to noise or incorporate information from other subjects, none of them tackles the nonstationarity problem directly. In this paper we propose a method which regularizes CSP towards stationary subspaces (sCSP) and show that this increases classification accuracy, especially for subjects who are hardly able to control a BCI. We compare our method with the stateoftheart approaches on different data sets, show competitive results and analyse the reasons for the improvement. Stationary Common Spatial Patterns for BCI 2 1.