machine learning for signal processing
TRANSCRIPT
ARTICLE IN PRESS
Neurocomputing 72 (2008) 1–2
Contents lists available at ScienceDirect
Neurocomputing
0925-23
doi:10.1
journal homepage: www.elsevier.com/locate/neucom
Editorial
Machine Learning for Signal Processing
This special issue on ‘‘Machine Learning for Signal Processing’’features a selection of extended versions of papers that haveoriginally been presented at the 2006 IEEE International Work-shop on Machine Learning for Signal Processing (MLSP2006) inMaynooth, Ireland (September 6–8) (formerly called the IEEEInternational Workshop on Neural Networks for Signal Processing,NNSP). The authors have been invited to contribute to this specialissue on the basis of originality, technical quality, and relevance.Also there is one contribution from the authors that won the 2006MLSP data competition. The invited papers have been subjected toa rigorous and anonymous peer review process. The guest editorsare convinced that this special issue provides the reader withinteresting examples of how machine learning can tackle today’schallenging signal processing problems.
The papers can be roughly grouped into the followingcategories: clustering and classification, Bayesian methods andgenerative modeling, signal separation, and applications.
1.
Clustering and classification: Renjifo and co-workers address thecomputational burden when classifiers, such as support vectormachines, are trained on large data sets. As a solution to this,the authors propose a new algorithm, called IncrementalAsymmetric Proximal SVM, that performs a greedy searchacross the data to select the basis vectors of the classifier,and then tunes the parameters automatically. Nelson andco-workers introduce a signal-theoretic method that limits therequired training and validation of the SVM classifier to a finitekernel hyper-parameter search using the sinc kernel. Themethod is adapted to the max sequence kernel, so that positivedefiniteness, and thus convergence, can be guaranteed. Jenssenand Eltoft introduce a new input space analysis of theproperties of the sum-of-squared-error K-means clusteringperformed with the Mercer kernel. Their derivation extendsthe theory of traditional K-means from properties of meanvectors to information theoretic properties of Parzen window-based probability density estimation.2.
Bayesian methods and generative modeling: Harva and Ray-chaudhury introduce a Bayesian method for estimating thetime delays between irregularly sampled signals. The posteriordistribution of the delay is obtained partly by an exactmarginalization of a specific type of Kalman filter and partlyby Markov chain Monte Carlo (MCMC) modeling. Klami andKaski study data fusion under the assumption that the datasource-specific variation is irrelevant and that only the sharedvariation is relevant. In order to tackle issues such asoverfitting and model order selection, which come with12/$ - see front matter & 2008 Elsevier B.V. All rights reserved.
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addressing shared variation by maximizing a dependencymeasure, such as with canonical correlation analysis (CCA),the authors turn to probabilistic generative modeling, which inturn makes all tools of Bayesian inference applicable.
3.
Signal separation: Lee and co-workers address the blind sourceseparation (BSS) problem by exploiting the prior knowledgethat the mixed sources are bounded. A customized contrastfunction is defined that relies on a simple endpoint estimator.Almeida and Almeida address the nonlinear separation ofmixtures of images that occur when a page is scanned orphotographed when the background shows through. Theauthors developed significant improvements of nonlineardenoising source separation (DSS) so that one-shot processing,rather that an iterative one, becomes possible. Radfar and co-workers perform speaker independent single-channel speechseparation. They fit a generative model to the envelopes of thelog spectra coming from different speakers, consider anexpression for the relation between this model and the densityof the mixture and the signal-to-signal ratio (SSR) and, finally,estimate the model parameters, along with the SSR, whichmaximize the log-likelihood of the mixture density. Vincentand Plumbley investigate a generic inference method based onan approximate factorization of the joint product of indepen-dent distributions of small subsets of parameters. Theyevaluate this method on the task of multiple pitch estimationusing different levels of factorization.4.
Applications: O’Grady and Pearlmutter develop an extension toconvolutive non-negative matrix factorization (NMF) thatincludes a sparseness constraint due to which auditory datacan be parsimoniously represented. In combination with aspectral magnitude transformation of speech signals, thedeveloped method detects auditory objects that resemblespeech phones. Jeong and co-workers apply the nonlinearextension of the minimum average correlation energy (MACE)filter, which relies on correntropy, to face recognition. Thecomputational cost of the correntropy MACE (CMACE) filter is acritical issue in applications, which the authors address with adimensionality reduction based on random projections. Redmondand co-workers describe a simple denoising technique, basedon spatial averaging, to reduce the number of trials needed toincrease the signal-to-noise level. They apply their techniqueto Magnetoencephalography (MEG) data. Miller and co-work-ers consider ensemble classification when there is no commonlabeled data for designing the function which aggregatesclassifier decisions. Classifier combinations such as votingmethods may perform poorly in this case. The authors proposeARTICLE IN PRESS
Editorial / Neurocomputing 72 (2008) 1–22
several transductive methods, of which a constraint-based oneseems to perform best. The new method is applied to biometricauthentication.
The editors would like to thank all the authors for their excellentpapers, and the anonymous reviewers for their comments and usefulsuggestions. Special thanks go to Dr. Tom Heskes for inviting us toedit this special issue, and to Vera Kamphuis from NeurocomputingEditorial Office for her help in putting it all together.
Marc M. Van HulleK.U.Leuven, Belgium
E-mail address: [email protected]
Jan LarsenTechnical University of Denmark, Denmark