Many automated systems need the capability of automatic change detection without the given detection threshold. This paper presents an automated change detection algorithm in streaming multivariate data. Two overlapping windows are used to quantify the changes. While a window is used as the reference window from which the clustering is created, the other called the current window captures the newly incoming data points. A newly incoming data point can be considered a change point if it is not a member of any cluster. As our clustering-based change detector does not require detection threshold, it is an automated detector. Based on this change detector, we propose a reactive clustering algorithm for streaming data. Our empirical results show that, our clustering-based change detector works well with multivariate streaming data. The detection accuracy depends on the number of clusters in the reference window, the window width.
The automatic selection of threshold is of special importance. @cite_2 have presented an automated change detection method for streaming data based on Hidden Markov Models. This HMM-based method is an automated change detection method by thresholding. Their algorithm consists of the following steps: model the relationships among data streams a sequence of time invariant linear dynamic system; model the evolution of the estimated parameters of the model by Hidden Markov Model; evaluate the likelihood of new parameters; detect change based on a given threshold. If the likelihood is less than a given threshold, a change is detected.
@cite_2
2037193886 2169784142 2120701504 1972250859
In this work we address the problem of automatically detecting changes either induced by faults or concept drifts in data streams coming from multi-sensor units. The proposed methodology is based on the fact that the relationships among different sensor measurements follow a probabilistic pattern sequence when normal data, i.e. data which do not present a change, are observed. Differently, when a change in the process generating the data occurs the probabilistic pattern sequence is modified. The relationship between two generic data streams is modelled through a sequence of linear dynamic time-invariant models whose trained coefficients are used as features feeding a Hidden Markov Model (HMM) which, in turn, extracts the pattern structure. Change detection is achieved by thresholding the log-likelihood value associated with incoming new patterns, hence comparing the affinity between the structure of new acquisitions with that learned through the HMM. Experiments on both artificial and real data demonstrate the appreciable performance of the method both in terms of detection delay, false positive and false negative rates. This paper describes a Hidden Markov Model (HMM)-based method of automatic transcription of MIDI (Musical Instrument Digital Interface) signals of performed music. The problem is formulated as recognition of a given sequence of fluctuating note durations to find the most likely intended note sequence utilizing the modern continuous speech recognition technique. Combining a stochastic model of deviating note durations and a stochastic grammar representing possible sequences of notes, the maximum likelihood estimate of the note sequence is searched in terms of Viterbi algorithm. The same principle is successfully applied to a joint problem of bar line allocation, time measure recognition, and tempo estimation. Finally, durations of consecutive spl eta n notes are combined to form a "rhythm vector" representing tempo-free relative durations of the notes and treated in the same framework. Significant improvements compared with conventional "quantization" techniques are shown. A new method is developed using the hidden Markov model (HMM) based technique. To handle nongesture patterns, we introduce the concept of a threshold model that calculates the likelihood threshold of an input pattern and provides a confirmation mechanism for the provisionally matched gesture patterns. The threshold model is a weak model for all trained gestures in the sense that its likelihood is smaller than that of the dedicated gesture model for a given gesture. Consequently, the likelihood can be used as an adaptive threshold for selecting proper gesture model. It has, however, a large number of states and needs to be reduced because the threshold model is constructed by collecting the states of all gesture models in the system. To overcome this problem, the states with similar probability distributions are merged, utilizing the relative entropy measure. Experimental results show that the proposed method can successfully extract trained gestures from continuous hand motion with 93.14 reliability. In unsupervised classification, Hidden Markov Models (HMM) are used to account for a neighborhood structure between observations. The emission distributions are often supposed to belong to some parametric family. In this paper, a semiparametric model where the emission distributions are a mixture of parametric distributions is proposed to get a higher flexibility. We show that the standard EM algorithm can be adapted to infer the model parameters. For the initialization step, starting from a large number of components, a hierarchical method to combine them into the hidden states is proposed. Three likelihood-based criteria to select the components to be combined are discussed. To estimate the number of hidden states, BIC-like criteria are derived. A simulation study is carried out both to determine the best combination between the combining criteria and the model selection criteria and to evaluate the accuracy of classification. The proposed method is also illustrated using a biological dataset from the model plant Arabidopsis thaliana. A R package HMMmix is freely available on the CRAN.