In this paper we investigate the exploitation of non linear scaling of distances for advanced nearest neighbor classification. Starting from the recently found relation between the Hypersphere Classifier (HC) [1] and the Adaptive Nearest Neighbor rule (ANN) [2], here we propose PowerHC, an improved version of HC in which distances are normalized using a non linear mapping; non linear scaling of data, whose usefulness for feature spaces has been already assessed, has been hardly investigated for distances. A thorough experimental evaluation, involving 24 datasets and a challenging real world scenario of seismic signal classification, confirms the suitability of the proposed approach.

The Rectified Nearest Feature Line Segment (RN-FLS) classifier is an improved version of the Nearest Feature Line (NFL) classification rule. RNFLS corrects two drawbacks of NFL, namely the interpolation and extrapolation inaccuracies, by applying two consecutive processes-segmentation and rectification - to the initial set of feature lines. The main drawbacks of this technique, occurring in both training and test phases, are the high computational cost of the rectification procedure and the exponential explosion of the number of lines. We propose a cheaper version of RNFLS, based on a characterization of the points that should form good lines. The characterization relies on a recent neighborhood-based principle that categorizes objects into four types: safe, borderline, rare and outliers, depending on the position of each point with respect to the other classes. The proposed approach represents a variant of RNFLS in the sense that it only considers lines between safe points. This allows a drastic reduction in the computational burden imposed by RNFLS. We carried out an empirical and thorough analysis based on different public data sets, showing that our proposed approach, in general, is not significantly different from RNFLS, but cheaper since the consideration of likely irrelevant feature line segments is avoided.

We present an online method to learn recurring time-frequency patterns from spectrograms. Our method relies on a convolutive decomposition that estimates sequences of spectra into time-frequency patterns and their corresponding activation signals. This method processes one spectrogram at a time such that in comparison with a batch method, the computational cost is reduced proportionally to the number of considered spectrograms. We use a first-order stochastic gradient descent and show that a monotonically decreasing learning-rate works appropriately. Furthermore, we suggest a framework to classify spectrograms based on the estimated set of time-frequency patterns. Results, on a set of synthetically generated spectrograms and a real-world dataset, show that our method finds meaningful time-frequency patterns and that it is suitable to handle a large amount of data.

The automated classification of seismic volcanic signals has been faced with several different pattern recognition approaches. Among them, hidden Markov models (HMMs) have been advocated as a cost-effective option having the advantages of a straightforward Bayesian interpretation and the capacity of dealing with seismic sequences of different lengths. In the volcano seismology scenario, HMM-based classification schemes were only based on a standard and purely generative scheme, i.e., the Bayes rule: training an HMM per class and classifying an incoming seismic signal according to the class whose model shows the highest likelihood. In this paper, a novel HMM-based classification approach for pretriggered seismic volcanic signals is proposed. The main idea is to enrich the classical HMM scheme with a discriminative step that is able to recover from situations when the classical Bayes classification rule is not sufficient. More in detail, a generative embedding scheme is used, which employs the models to map the signals into a vector space, which is called generative embedding space. In such a space, any discriminative vector-based classifier can be applied. A thorough set of experiments, which is carried out on pretriggered signals recorded at Galeras Volcano in Colombia, shows that the proposed approach typically outperforms standard HMM-based classification schemes, also in some cross-station cases.

Dissimilarities can be a powerful way to represent objects like strings, graphs and images for which it is difficult to find good features. The resulting dissimilarity space may be used to train any classifier appropriate for feature spaces. There is, however, a strong need for dimension reduction. Straightforward procedures for prototype selection as well as feature selection have been used for this in the past. Complicated sets of objects may need more advanced procedures to overcome local minima. In this paper it is shown that genetic algorithms, previously used for feature selection, may be used for building good dissimilarity spaces as well, especially when small sets of prototypes are needed for computational reasons.

Seismic events in and around volcanos, like tremors, earth quakes, ice quakes and strokes of lightning, are usually observed by multiple stations. The question rises whether classifiers trained for one seismic station can be used for classifying observations by other stations, and, moreover, whether a combination of station signals improves the classification performances for a single station. We study this for seismic time signals represented by spectra and spectrograms obtained from 5 seismic stations on the Nevado del Ruiz in Colombia.

Multi-way data analysis is a multivariate data analysis technique having a wide application in some fields. Nevertheless, the development of classification tools for this type of representation is incipient yet. In this paper we study the dissimilarity representation for the classification of three-way data, as dissimilarities allow the representation of multi-dimensional objects in a natural way. As an example, the classification of seismic volcanic events is used. It is shown that in this application classification based on 2D spectrograms, dissimilarities perform better than on 1D spectral features.