Unsupervised Adaptation Algorithm Research Articles

Unsupervised online classifier in sleep scoring for sleep deprivation studies.

This study was designed to evaluate an unsupervised adaptive algorithm for real-time detection of sleep and wake states in rodents. We designed a Bayesian classifier that automatically extracts electroencephalogram (EEG) and electromyogram (EMG) features and categorizes non-overlapping 5-s epochs into one of the three major sleep and wake states without any human supervision. This sleep-scoring algorithm is coupled online with a new device to perform selective paradoxical sleep deprivation (PSD). Controlled laboratory settings for chronic polygraphic sleep recordings and selective PSD. Ten adult Sprague-Dawley rats instrumented for chronic polysomnographic recordings. The performance of the algorithm is evaluated by comparison with the score obtained by a human expert reader. Online detection of PS is then validated with a PSD protocol with duration of 72 hours. Our algorithm gave a high concordance with human scoring with an average κ coefficient > 70%. Notably, the specificity to detect PS reached 92%. Selective PSD using real-time detection of PS strongly reduced PS amounts, leaving only brief PS bouts necessary for the detection of PS in EEG and EMG signals (4.7 ± 0.7% over 72 h, versus 8.9 ± 0.5% in baseline), and was followed by a significant PS rebound (23.3 ± 3.3% over 150 minutes). Our fully unsupervised data-driven algorithm overcomes some limitations of the other automated methods such as the selection of representative descriptors or threshold settings. When used online and coupled with our sleep deprivation device, it represents a better option for selective PSD than other methods like the tedious gentle handling or the platform method.

Unsupervised adaptation of electroencephalogram signal processing based on fuzzy C‐means algorithm

SUMMARYThis paper studies an unsupervised approach for online adaptation of electroencephalogram (EEG) based brain–computer interface (BCI). The approach is based on the fuzzy C‐means (FCM) algorithm. It can be used to improve the adaptability of BCIs to the change in brain states by online updating the linear discriminant analysis classifier. In order to evaluate the performance of the proposed approach, we applied it to a set of simulation data and compared with other unsupervised adaptation algorithms. The results show that the FCM‐based algorithm can achieve a desirable capability in adapting to changes and discovering class information from unlabeled data. The algorithm has also been tested by the real EEG data recorded in experiments in our laboratory and the data from other sources (set IIb of the BCI Competition IV). The results of real data are consistent with that of simulation data. Copyright © 2011 John Wiley & Sons, Ltd.

Unsupervised Acoustic Model Adaptation Based on Ensemble Methods

We propose unsupervised cross-validation (CV) and aggregated (Ag) adaptation algorithms that integrate the ideas of ensemble methods, such as CV and bagging, in the iterative unsupervised batch-mode adaptation framework. These algorithms are used to reduce overtraining problems and to improve speech recognition performance. The algorithms are constructed on top of a general parameter estimation technique such as the maximum-likelihood linear regression method. The proposed algorithms are also useful for suppressing the negative effects of unsupervised adaptation, which reinforces the errors included in the hypothesis used for the adaptation. Experiments are performed using clean and noisy speech recognition tasks with several conditions. We show that both our proposed unsupervised adaptation algorithms give higher performance than the conventional batch-mode adaptation algorithm; however, the unsupervised CV adaptation algorithm is more advantageous than the unsupervised Ag adaptation algorithm in terms of computational cost. The proposed algorithms resulted in 4% to 10% relative reduction in the word error rate over the conventional batch-mode adaptation.

Effective online unsupervised adaptation of Gaussian mixture models and its application to speech classification

Online unsupervised adaptation of statistical classifiers is attractive for many speech processing applications. In this work, we describe an online unsupervised adaptation method for a four-way speech classifier which is based on modelling the universal background model (UBM)–GMM and using confidence scoring in deriving classification results. The aim of the proposed method is to automatically adapt the classifier to mismatched conditions caused by acoustically adverse backgrounds and speaker variability. Extensive analysis of the experimental learning curves shows that the new online unsupervised adaptation algorithm achieves practical convergence. When compared to batch mode adaptation the proposed technique deals effectively with data sparsity and it has significantly lower computational requirements at the expense of a slight sacrifice in classification performance. The proposed algorithm can be readily extended to other mixture families and different expectation–maximization (EM) alternatives for improved performance.