Classification Based on Compressive Multivariate Time Series

Utomo, Chandra Prasetyo; Li, Xue; Wang, Sen

doi:10.1007/978-3-319-46922-5_16

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…Nanopoulos et al [26] proposed a method based on statistical feature extraction to establish feature vectors, and then trained a multi-layer perceptron (MLP) from the feature vectors and target categories to achieve the classification of time series. Utomo et al [27] proposed to classify the multidimensional data of the hospital intensive care unit by the method of multidimensional compression description (MultiCoRe), which extracted the features including time domain and frequency domain for classification. Jaakkola et al [28] introduced a method combining HMM and SVM for classifying protein domains.…”

Section: Related Workmentioning

confidence: 99%

Nonparametric Bayesian Method for Robot Anomaly Diagnose

Zhou

Rojas

et al. 2020

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

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In this chapter, we introduce two novel anomaly diagnose methods using the Bayesian nonparametric hidden Markov models when anomaly triggered, including i)multi-class classifier based on nonparametric models, ii) sparse representation by statistical feature extraction for anomaly diagnose. Additionally, the detail procedure for anomaly sample definition, the supervised learning dataset collection as well as the data augmentation of insufficient samples are also declared. We evaluated the proposed methods with a multi-step human-robot collaboration objects kitting task on Baxter robot, the performance and results are presented of each method respectively.

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Section: Related Workmentioning

confidence: 99%

Nonparametric Bayesian Method for Robot Anomaly Diagnose

Zhou

Rojas

et al. 2020

Nonparametric Bayesian Learning for Collaborative Robot Multimodal Introspection

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“…Exploration of time series data [114,122,97,135] is evident in many domains (e.g., financial, medical and environmental domains). One of the key analysis tasks in these domains is to detect patterns or anomalies among multiple time series [76,72].…”

Section: Refinement 41 Overviewmentioning

confidence: 99%

“…Moreover, time series data in the medical domain contain vital information. Examining and analyzing these series to detect unusual patterns can significantly save a human life [122,89].…”

Section: Refinement 41 Overviewmentioning

confidence: 99%

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Similarity-aware query refinement for data exploration

Albarrak¹

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Database users are easily overwhelmed by the sheer size of data found in large-scale scientific and financial databases. Exploring these databases to make sense of the explored data and to discover interesting insights (i.e., data exploration) has been, and still is, a hideous and labour-intensive task, especially for non-expert users with no solid background of the underlying data. Some three decades ago, the database research community noticed the limitation of traditional DBMS in supporting users for data exploration tasks. Since then, the research community has proposed and designed various effective and efficient data exploration techniques to assist users in extracting interesting insights from their data. An instance of these techniques is the Query Refinement technique. In query refinement techniques, users' queries are assumed to be imprecise, i.e., the returned result does not meet some user-defined constraints. Accordingly, the goal of query refinement techniques is to automatically refine these imprecise queries to maximize users' satisfaction with the results. In particular, the predicates of the queries are carefully modified so that the returned results satisfy the user-defined constraints. Since users' constraints on the queries results are diverse and miscellaneous, this thesis focuses on two specific forms of constraints in exploring relational and sequential data, namely, 1) user-defined aggregate constraints on the result, and 2) user-defined correlation constraints of time series data. These constraints are common in real world applications because they represent an upper level view of the result that is easier to understand and digest than the raw result itself. This thesis addresses the limitations of current query refinement techniques that are oblivious to the similarity of the refined queries to the users' initial queries. Specifically, users' initial (and imprecise) queries are defined as anchor points for which the similarity of its corresponding refined queries are computed over the whole refinement space. Consequently, the similarity-aware query refinement problem is formulated as a search problem, which aims to balance the trade-off between minimizing the deviation from satisfying a constraint on the query result, and maximizing the similarity of the refined query to the initial one. Searching for a trade-off between satisfying a constraint on the result of a query and maximizing the similarity introduces various challenges. A common challenge shared by many query refinement problems is that finding an optimal trade-off i Lastly, I would like to formally thank my sponsor, Al-Imam Muhammad Ibn Saud Islamic University, for providing the financial support which made this journey possible.

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