Clustering objects into synthetic groups is a natural activity of any science. Astrophysics is not an exception and is now facing a deluge of data. For galaxies, the one-century old Hubble classification and the Hubble tuning fork are still largely in use, together with numerous monoor bivariate classifications most often made by eye. However, a classification must be driven by the data, and so...

Early classification of time series has been receiving a lot of attention recently. In this paper we present a model, which we call the Early Classification Model (ECM), that allows for early, accurate and patient-specific classification of multivariate observations. ECM is comprised of an integration of the widely used Hidden Markov Model (HMM) and Support Vector Machine (SVM) models. It attai...

One of the important problems in many process industries is how to predict the occurrence of abnormal situations ahead of time in a multivariate time series environment. For example, in an oil refinery, hundreds of sensors (process variables) are installed at different sections of a process unit. These sensors constantly monitor the development of every stage of the process. Typically, each pro...

We illustrate unsupervised clustering algorithms using a twodimensional color-magnitude diagram constructed from the COMBO-17 (`Classifying Objects by Medium-Band Observations in 17 Filters') photometric survey of normal galaxies (Wolf et al. 2003). The R script below starts with the which function to filter the dataset, keeping only low-redshift galaxies with z < 0.3 and remove a few points wi...

Distribution-mapping exponent (DME) that is something like effective dimensionality of multidimensional space is introduced. The method for classification of multivariate data is based on local estimate of distribution mapping exponent for each point. Distances of all points of a given class of the training set from a given (unknown) point are searched and it is shown that the sum of reciprocal...

Over the past decade, multivariate time series classification has been receiving a lot of attention. We propose augmenting the existing univariate time series classification models, LSTM-FCN and ALSTM-FCN with a squeeze and excitation block to further improve performance. Our proposed models outperform most of the state of the art models while requiring minimum preprocessing. The proposed model...

In many engineering applications, data samples are expensive to get and limited in number. In such a difficult context, this paper shows how classification based on Reproducing Kernel Hilbert Space (RKHS) can be used in conjunction with Extreme Value Theory (EVT) to estimate extreme multivariate quantiles and small probabilities of failure. For estimating extreme multivariate quantiles, RKHS on...

Multivariate time series (MTS) arise when multiple interconnected sensors record data over time. Dealing with this high-dimensional data is challenging for every classifier for at least two aspects: First, a MTS is not only characterized by individual feature values, but also by the co-occurrence of features in different dimensions. Second, this typically adds large amounts of irrelevant data a...