Mauricio Orozco-Alzate

Classification of three-way data by the dissimilarity representation

Representation of objects by multi-dimensional data arrays has become very common for many research areas e.g. image analysis, signal processing and chemometrics. In most cases, it is the straightforward representation obtained from sophisticated measurement equipments e.g. radar signal processing. Although the use of this complex data structure could be advantageous for a better discrimination between different classes of objects, it is usually ignored. Classification tools that take this structure into account have hardly been developed yet. Meanwhile, the dissimilarity representation has demonstrated advantages in the solution of classification problems e.g. spectral data. Dissimilarities also allow the representation of multi-dimensional objects in a way that the data structure can be used. This paper introduces their use as a tool for classifying objects originally represented by two-dimensional (2D) arrays. 2D measures can be useful to achieve this representation. A 2D measure to compute the dissimilarity representation from spectral data with this kind of structure is proposed. It is compared to existent 2D measures, in terms of the information that is taken into account and computational complexity.

A generalization of dissimilarity representations using feature lines and feature planes

Even though, under representational restrictions, the nearest feature rules and the dissimilarity-based classifiers are feasible alternatives to the nearest neighbor method; individually, they may not be sufficiently powerful if a very small set of prototypes is required, e.g. when it is computationally expensive to deal with larger sets of prototypes. In this paper, we show that combining both strategies, taking advantage of their individual properties, provides an improvement, particularly for correlated data sets. The combined strategy consists in deriving an enriched (generalized) dissimilarity representation by using the nearest feature rules, namely feature lines and feature planes. On top of that enriched representation, Bayesian classifiers can be constructed in order to obtain a good generalization.

Comparison of the nearest feature classifiers for face recognition

This paper presents an experimental comparison of the nearest feature classifiers, using an approach based on binomial tests in order to evaluate their strengths and weaknesses. In addition, classification accuracies and the accuracy-dimensionality tradeoff have been considered as comparison criteria. We extend two of the nearest feature classifiers to label the query point by a majority vote of the samples. Comparisons were carried out for face recognition using ORL database. We apply the eigenface representation for feature extraction. Experimental results showed that even though the classification accuracy of k-NFP outperforms k-NFL in some dimensions, these rate differences do not have statistical significance.

Classification of Seismic Volcanic Signals Using Hidden-Markov-Model-Based Generative Embeddings

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.

Metric Learning in Dissimilarity Space for Improved Nearest Neighbor Performance

Showing the nearest neighbor is a useful explanation for the result of an automatic classification. Given, expert defined, distance measures may be improved on the basis of a training set. We study several proposals to optimize such measures for nearest neighbor classification, explicitly including non-Euclidean measures. Some of them may directly improve the distance measure, others may construct a dissimilarity space for which the Euclidean distances show significantly better performances. Results are application dependent and raise the question what characteristics of the original distance measures influence the possibilities of metric learning.

On the informativeness of asymmetric dissimilarities

A widely used approach to cope with asymmetry in dissimilarities is by symmetrizing them. Usually, asymmetry is corrected by applying combiners such as average, minimum or maximum of the two directed dissimilarities. Whether or not these are the best approaches for combining the asymmetry remains an open issue. In this paper we study the performance of the extended asymmetric dissimilarity space (EADS) as an alternative to represent asymmetric dissimilarities for classification purposes. We show that EADS outperforms the representations found from the two directed dissimilarities as well as those created by the combiners under consideration in several cases. This holds specially for small numbers of prototypes; however, for large numbers of prototypes the EADS may suffer more from overfitting than the other approaches. Prototype selection is recommended to overcome overfitting in these cases.

Selection of time-variant features for earthquake classification at the Nevado-del-Ruiz volcano

Seismic event recognition is an important task for hazard assessment, eruption prediction and risk mitigation, since it can be used to determine the state of a volcano. Usually, expert technicians read features extracted from the seismogram, such as, cepstral derived coefficients, energy centroids, instant frequency, instant envelop, among others. However, there are few studies about the selection of important features for classifying several types of seismic events, i.e., taking into account the temporal contribution of each considered feature. This paper presents a feature selection strategy based on a relevance measure of time-variant features for seismic event classification. In this research, features are selected as those with the maximal information preserved within the time analysis. Since features selection stage is performed by incremental training, a simple k-nearest neighbor classification rule is used to properly determine the dimension of the final feature set. The employed feature extraction and feature selection methodologies are tested on an isolated event recognition task. The database used to test the methodology is composed of the following classes: volcano-tectonic, long period earthquakes, tremors and hybrid events. Data was recorded at the seismic monitoring stations located at the Nevado-del-Ruiz volcano, Colombia. Using a classifier based on hidden Markov models, accomplished results exhibit a performance improvement from 78% to 88% using the proposed methodology in comparison to the state-of-the-art feature sets.

The Automated Identification of Volcanic Earthquakes: Concepts, Applications and Challenges

Classifying seismic signals into their corresponding types of volcanic earthquakes is among the most important tasks for monitoring volcano activity. Such a duty must be routinely conducted—in a daily basis— and implies, therefore, a significant workload for the personnel. The discipline of pattern recognition (PR) provides volcanic seismology practitioners with theories and methods to design classification systems and, together with digital signal processing (DSP) techniques, has given rise to promising and challenging opportunities for the automated identification of volcanic earthquakes.

Dissimilarity representation on functional spectral data for classification

In chemometrics, spectral data are typically represented by vectors of features in spite of the fact that they are usually plotted as functions of e.g. wavelengths and concentrations. In the representation, this functional information is thereby not reflected. Consequently, some characteristics of the data that can be essential for discrimination between samples of different classes or any other analysis are ignored. Examples are the continuity between measured points and the shape of curves. In the Functional Data Analysis (FDA) approach, the functional characteristics of spectra are taken into account by approximating the data by real valued functions, e.g. splines. Another solution is the Dissimilarity Representation (DR), in which classifiers are trained in a space built by dissimilarities with training examples or prototypes of each class. Functional information may be incorporated in the definition of the dissimilarity measure. In this paper we compare the feature‐based representation of chemical spectral data with three other representations: FDA, DR defined on raw data and DR defined on FDA descriptions. We analyze the classification results of these four representations for five data sets of different types, by using different classifiers. We demonstrate the importance of reflecting the functional characteristics of chemical spectral data in their representation, and we show when the presented approaches are more suitable. Copyright

The DTW-based representation space for seismic pattern classification

Distinguishing among the different seismic volcanic patterns is still one of the most important and labor-intensive tasks for volcano monitoring. This task could be lightened and made free from subjective bias by using automatic classification techniques. In this context, a core but often overlooked issue is the choice of an appropriate representation of the data to be classified. Recently, it has been suggested that using a relative representation (i.e. proximities, namely dissimilarities on pairs of objects) instead of an absolute one (i.e. features, namely measurements on single objects) is advantageous to exploit the relational information contained in the dissimilarities to derive highly discriminant vector spaces, where any classifier can be used. According to that motivation, this paper investigates the suitability of a dynamic time warping (DTW) dissimilarity-based vector representation for the classification of seismic patterns. Results show the usefulness of such a representation in the seismic pattern classification scenario, including analyses of potential benefits from recent advances in the dissimilarity-based paradigm such as the proper selection of representation sets and the combination of different dissimilarity representations that might be available for the same data. HighlightsA representation, based on the DTW measure, is proposed for seismic classification.Recent advances of the dissimilarity based representation are investigated for DTW.Experiments with large scope dataset confirm the suitability of the DTW-space.The proposed space, when derived from spectrograms, is the best representation.Selecting small representation sets reduces the number of required DTW comparisons.