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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26 May 2011
TL;DR: This paper studies CPU utilization time patterns of several MapReduce applications to evaluate the hypothesis in tweaking system parameters in executing similar applications, and results showed effectiveness of the approach on pseudo-distributed Map Reduce platforms.
Abstract: In this paper, we study CPU utilization time patterns of several MapReduce applications. After extracting running patterns of several applications, they are saved in a reference database to be later used to tweak system parameters to efficiently execute unknown applications in future. To achieve this goal, CPU utilization patterns of new applications are compared with the already known ones in the reference database to find/predict their most probable execution patterns. Because of different patterns lengths, the Dynamic Time Warping (DTW)is utilized for such comparison, a correlation analysis is then applied to DTWs' outcomes to produce feasible similarity patterns. Three real applications (Word Count, Exim Mainlogparsing and Terasort) are used to evaluate our hypothesis in tweaking system parameters in executing similar applications. Results were very promising and showed effectiveness of our approach on pseudo-distributed MapReduce platforms
33 citations
TL;DR: A novel multi-layer perceptrons (MLP)-based speech recognition method that is comparable to a well modeled continuous Gaussian mixture density HMM trained with the minimum error criterion and requires less trainable parameters than the HMM system, but the former is more convenient for analysing internal features.
33 citations
Proceedings Article•
01 Jan 2011TL;DR: A novel lower-bound estimate for dynamic time warping methods that use an inner product distance on multi-dimensional posterior probability vectors known as posteriorgrams that is able to speed up DTWKNN search by 28% without affecting the keyword spotting performance.
Abstract: In this paper, we propose a novel lower-bound estimate for dynamic time warping (DTW) methods that use an inner product distance on multi-dimensional posterior probability vectors known as posteriorgrams. Compared to our previous work, the new lower-bound estimate uses piecewise aggregate approximation (PAA) to reduce the time required for calculating the lower-bound estimate. We describe the PAA lower-bound construction process and prove that it can be efficiently used in an admissible K nearest neighbor (KNN) search. The amount of computational savings is quantified by a set of unsupervised spoken keyword spotting experiments. The results show that the newly proposed PAA lower-bound is able to speed up DTWKNN search by 28% without affecting the keyword spotting performance.
33 citations
08 Sep 2018
TL;DR: In this paper, a triplet-based deep metric learning approach is proposed to deal with human motion data, in particular with the problem of varying input size and computationally expensive hard negative mining due to motion pair alignment.
Abstract: Effectively measuring the similarity between two human motions is necessary for several computer vision tasks such as gait analysis, person identification and action retrieval. Nevertheless, we believe that traditional approaches such as L2 distance or Dynamic Time Warping based on hand-crafted local pose metrics fail to appropriately capture the semantic relationship across motions and, as such, are not suitable for being employed as metrics within these tasks. This work addresses this limitation by means of a triplet-based deep metric learning specifically tailored to deal with human motion data, in particular with the problem of varying input size and computationally expensive hard negative mining due to motion pair alignment. Specifically, we propose (1) a novel metric learning objective based on a triplet architecture and Maximum Mean Discrepancy; as well as, (2) a novel deep architecture based on attentive recurrent neural networks. One benefit of our objective function is that it enforces a better separation within the learned embedding space of the different motion categories by means of the associated distribution moments. At the same time, our attentive recurrent neural network allows processing varying input sizes to a fixed size of embedding while learning to focus on those motion parts that are semantically distinctive. Our experiments on two different datasets demonstrate significant improvements over conventional human motion metrics.
33 citations
23 Aug 2005
TL;DR: An improved DTW (Dynamic Time Warping) algorithm is put forward to verify online signatures based on writing forces to deal with the varying consistency of signature point, signing duration and the different weights of writing forces in different direction.
Abstract: Writing forces are important dynamics of online signatures and they are harder to be imitated by forgers than signature shape. An improved DTW (Dynamic Time Warping) algorithm is put forward to verify online signatures based on writing forces. Compared to the general DTW algorithm, this one deals with the varying consistency of signature point, signing duration and the different weights of writing forces in different direction. The iterative dexperiment is introduced to decide weights for writing forces in different direction and the classification threshold. A signature database is constructed with F_Tablet and the equal error rate of 1.4% is realized with the improved algorithm.
33 citations