Time Series Clustering Via RPCL Network Ensemble With Different Representations

Yang, Yun; Chen, Ke

doi:10.1109/tsmcc.2010.2052608

Cited by 38 publications

(7 citation statements)

References 34 publications

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“…In general, model-based approaches suffer from scalability issues [35]. Yang et al [36] presented an unsupervised ensemble learning approach to time series clustering using a combination of RPCL (rival penalized competitive learning) with other representations.…”

Section: A Time Series Clusteringmentioning

confidence: 99%

Time-Series Clustering Based on the Characterization of Segment Typologies

Guijo-Rubio

Durán-Rosal

Gutiérrez

et al. 2021

IEEE Trans. Cybern.

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Time series clustering is the process of grouping time series with respect to their similarity or characteristics. Previous approaches usually combine a specific distance measure for time series and a standard clustering method. However, these approaches do not take the similarity of the different subsequences of each time series into account, which can be used to better compare the time series objects of the dataset. In this paper, we propose a novel technique of time series clustering based on two clustering stages. In a first step, a least squares polynomial segmentation procedure is applied to each time series, which is based on a growing window technique that returns different-length segments. Then, all the segments are projected into same dimensional space, based on the coefficients of the model that approximates the segment and a set of statistical features. After mapping, a first hierarchical clustering phase is applied to all mapped segments, returning groups of segments for each time series. These clusters are used to represent all time series in the same dimensional space, after defining another specific mapping process. In a second and final clustering stage, all the time series objects are grouped. We consider internal clustering quality to automatically adjust the main parameter of the algorithm, which is an error threshold for the segmentation. The results obtained on 84 datasets from the UCR Time Series Classification Archive have been compared against two state-of-the-art methods, showing that the performance of this methodology is very promising.

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Section: A Time Series Clusteringmentioning

confidence: 99%

Time-Series Clustering Based on the Characterization of Segment Typologies

Guijo-Rubio

Durán-Rosal

Gutiérrez

et al. 2021

IEEE Trans. Cybern.

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“…CNNs are composed of a convolution layer, a pooling layer, and a full connection layer, which can process data of multiple arrays, for instance, 1D for time series data [29], [30]; 2D for images [31]; and 3D for video. There are three key technique support CNNs that profit from the properties of ideas: shared weights, local connections and use of many layers.…”

Section: A Convolution Neural Networkmentioning

confidence: 99%

A Stacked Multi-Granularity Convolution Denoising Auto-Encoder

Cao

Liu

et al. 2019

IEEE Access

Self Cite

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With the development of big data, artificial intelligence has provided many intelligent solutions to urban life. For instance, an image-based intelligent technology, such as image classification of diseases, is widely used in daily life. However, the image in real life is mostly unlabeled, so the performance of many image-based intelligent models shows limitations. Therefore, how to use a large amount of unlabeled image data to build an efficient and high-quality model for better urban life has been an urgent research topic. In this paper, we propose an unsupervised image feature extraction method that is referred to as a stacked multigranularity convolution denoising auto-encoder (SMGCDAE). The algorithm is based on a convolutional neural network (CNN), yet it introduces a multi-granularity kernel. This approach resolved issues with image unicity by extracting a diverse category of high-level features. In addition, the denoising auto-encoder ensures stability and improves the classification accuracy by extracting more robust features. The algorithm was assessed using three image benchmark datasets and a series of meningitis images, achieving higher average accuracy than other methods. These results suggest that the algorithm is capable of extracting more discriminative high-level features and thus offers superior performance compared with the existing methodologies.INDEX TERMS Unsupervised learning, feature extraction, denoising auto-encoder, convolutional neural network.

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“…We have developed several ensemble approaches [50][51][52][53][54][55] for unsupervised learning tasks. These attempt to improve the robustness of the learning process by combining multiple base learners into a solution, which normally is generally obtained with respect to the average performance of a given individual base learner, leading an effective enabling technique for the joint use of different representations in many pattern recognition systems [56][57][58].…”

Section: Weighted Ensemble Of Matching Modelsmentioning

confidence: 99%

Ensemble Learning‐Based Person Re‐identification with Multiple Feature Representations

Yang

Liu

et al. 2018

Complexity

Self Cite

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As an important application in video surveillance, person reidentification enables automatic tracking of a pedestrian through different disjointed camera views. It essentially focuses on extracting or learning feature representations followed by a matching model using a distance metric. In fact, person reidentification is a difficult task because, first, no universal feature representation can perfectly identify the amount of pedestrians in the gallery obtained by a multicamera system. Although different features can be fused into a composite representation, the fusion still does not fully explore the difference, complementarity, and importance between different features. Second, a matching model always has a limited amount of training samples to learn a distance metric for matching probe images against a gallery, which certainly results in an unstable learning process and poor matching result. In this paper, we address the issues of person reidentification by the ensemble theory, which explores the importance of different feature representations, and reconcile several matching models on different feature representations to an optimal one via our proposed weighting scheme. We have carried out the simulation on two well-recognized person reidentification benchmark datasets: VIPeR and ETHZ. The experimental results demonstrate that our approach achieves state-of-the-art performance.

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Time Series Clustering Via RPCL Network Ensemble With Different Representations

Cited by 38 publications

References 34 publications

Time-Series Clustering Based on the Characterization of Segment Typologies

Time-Series Clustering Based on the Characterization of Segment Typologies

A Stacked Multi-Granularity Convolution Denoising Auto-Encoder

Ensemble Learning‐Based Person Re‐identification with Multiple Feature Representations

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