Topic
Dynamic time warping
About: Dynamic time warping is a research topic. Over the lifetime, 6013 publications have been published within this topic receiving 133130 citations.
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TL;DR: The extensive evaluation of the proposed augmentation method reveals that the introduced method outperforms related augmentation algorithms in terms of the obtained classification accuracy.
Abstract: In this paper, a novel data augmentation method for time-series classification is proposed. In the introduced method, a new time-series is obtained in warped space between suboptimally aligned input examples of different lengths. Specifically, the alignment is carried out constraining the warping path and reducing its flexibility. It is shown that the resultant synthetic time-series can form new class boundaries and enrich the training dataset. In this work, the comparative evaluation of the proposed augmentation method against related techniques on representative multivariate time-series datasets is presented. The performance of methods is examined using the nearest neighbor classifier with the dynamic time warping (NN-DTW), LogDet divergence-based metric learning with triplet constraints (LDMLT), and the recently introduced time-series cluster kernel (NN-TCK). The impact of the augmentation on the classification performance is investigated, taking into account entire datasets and cases with a small number of training examples. The extensive evaluation reveals that the introduced method outperforms related augmentation algorithms in terms of the obtained classification accuracy.
40 citations
04 May 2015
TL;DR: A non-linear similarity metric based on a Siamese Neural Network (SNN), trained using a new error function that models the relations between pairs of similar and dissimilar samples in order to structure the network output space is proposed.
Abstract: In this paper, we tackle the task of symbolic gesture recognition using inertial MicroElectroMechanicals Systems (MEMS) present in Smartphones. We propose to build a non-linear similarity metric based on a Siamese Neural Network (SNN), trained using a new error function that models the relations between pairs of similar and dissimilar samples in order to structure the network output space. Experiments performed on different datasets regrouping up to 22 individuals and 18 gesture classes, targeting the most likely real case applications, show that this structure allows for an improved classification and a higher rejection quality over the conventional MultiLayer Perceptron (MLP) and Dynamic Time Warping (DTW) similarity metric.
40 citations
TL;DR: This work proposes an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class.
Abstract: We present an application of gesture recognition using an extension of dynamic time warping (DTW) to recognize behavioral patterns of attention deficit hyperactivity disorder (ADHD). We propose an extension of DTW using one-class classifiers in order to be able to encode the variability of a gesture category, and thus, perform an alignment between a gesture sample and a gesture class. We model the set of gesture samples of a certain gesture category using either Gaussian mixture models or an approximation of convex hulls. Thus, we add a theoretical contribution to classical warping path in DTW by including local modeling of intraclass gesture variability. This methodology is applied in a clinical context, detecting a group of ADHD behavioral patterns defined by experts in psychology/psychiatry, to provide support to clinicians in the diagnose procedure. The proposed methodology is tested on a novel multimodal dataset (RGB plus depth) of ADHD children recordings with behavioral patterns. We obtain satisfying results when compared to standard state-of-the-art approaches in the DTW context.
40 citations
TL;DR: It is shown that local distance features can be used as supplementary input information in temporal CNNs by using both the raw data and the features extracted from DTW in multi-modal fusion CNNs.
40 citations
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TL;DR: This article proposed to construct a latent multi-view graph to capture various possible relationships among tokens, and then refine this graph to select important words for relation prediction, finally, the representation of the refined graph and the BERT-based sequence representation are concatenated for relation extraction.
Abstract: Relation Extraction (RE) is to predict the relation type of two entities that are mentioned in a piece of text, e.g., a sentence or a dialogue. When the given text is long, it is challenging to identify indicative words for the relation prediction. Recent advances on RE task are from BERT-based sequence modeling and graph-based modeling of relationships among the tokens in the sequence. In this paper, we propose to construct a latent multi-view graph to capture various possible relationships among tokens. We then refine this graph to select important words for relation prediction. Finally, the representation of the refined graph and the BERT-based sequence representation are concatenated for relation extraction. Specifically, in our proposed GDPNet (Gaussian Dynamic Time Warping Pooling Net), we utilize Gaussian Graph Generator (GGG) to generate edges of the multi-view graph. The graph is then refined by Dynamic Time Warping Pooling (DTWPool). On DialogRE and TACRED, we show that GDPNet achieves the best performance on dialogue-level RE, and comparable performance with the state-of-the-arts on sentence-level RE.
40 citations