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Time Series Clustering and Classification最新文献

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Feature-based approaches 基于特征的方法
Time Series Clustering and Classification
Pub Date : 2019-03-19 DOI: 10.1201/9780429058264-9
E. Maharaj, P. D’Urso, Jorge Caiado
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引用次数: 0
Other time series clustering approaches 其他时间序列聚类方法
Time Series Clustering and Classification
Pub Date : 2019-03-19 DOI: 10.1201/9780429058264-8
E. Maharaj, P. D’Urso, Jorge Caiado
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引用次数: 0
Observation-based clustering 基于聚类
Time Series Clustering and Classification
Pub Date : 2019-03-19 DOI: 10.1201/9780429058264-5
E. Maharaj, P. D’Urso, Jorge Caiado
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引用次数: 0
Fuzzy clustering 模糊聚类
Time Series Clustering and Classification
Pub Date : 2019-03-19 DOI: 10.1201/9780429058264-4
E. Maharaj, P. D’Urso, Jorge Caiado
フ ァ ジ ィ ・ク ラス タ リン グ (Fuzzy Clustering) 有限個の個体に つ い て の 多変量デ ー タが 与えられたとき、これらの 個体を互い に重なりあわない い くつ か の クラス に分 け、同じクラス に はい る個体はJhl い !こ距離が近く、 異なるクラス に はい る個体は距離が遠い ように分類することをクラス タリン グとい う。ファ ジィ ・クラス タリン グ とは、通 常 、 次 の A 、 B の い ずれ か を意味する。 A .クリス プ分割 (共通部分が 空 で、すべ ての合併が個体全体 の 集合に致するような部分集合の 族の こと、分類ともい う。 )の 代りにファジィ分割を利用す るもの 。B .個体の 集合上 の フ ァ ジィ同値関係をデ ータから構成するもの 。 A とB につ い て代表的な手法 を次に挙げる。 A .(Fuzzy c −means )n 個の個体に つ い て の データが γ 次元 空間 の点 褊 k = 1,.., 12,で 表されるとする。 c 個の ファ ジィ集合からなるファジィ分割とは、ΣtUi(x ,) =1, k=1,..,n ,を満たすIti, i =・1,..,c,を意味する。ファジィ分 割を決定 するため 、晦 μ、(κh)に関する最適化問題 :min Σ tk (eq・,) II Xh− Vi ll Z (Il Ilは L2ノル ム )を解く。 ここで、最適化は 砺 i= 1 , ..,c;k ; 1,..,n ,および Vi, i=一1,..,・c,につ い て行う。 制約条件として 、 晦 が ファジィ分割を表わすように、 0≦ 晦 ≦Lfor a1141e ;Σ、 uilt =1, for all k,が 課せ られる。また、 m は m > 1 となる指数 ラメータである。 Bezdek
Fuzzy Clustering:当对有限个体给定多变量数据时，如何将这些个体不相互重叠?的班级，在同一班级的个体是Jhl !个体距离较近，而属于不同类别的个体则以距离较远的方式进行分类。所谓fazy krastaring，是指通常、次A、B的异同化。A .克里斯普分割(公共部分为空，所有的合并变成个体全体的集合的子集族，也称为分类。)而利用模糊分割。B .由数据构成个体集合上的二元等价关系。下面列举A和B的代表性手法。a . (fuzzy c - means) n个个体个γ-维数据空间的分褊k = 1, . .，用12来表示。c个化ジィ集合组成ファジィ分割,σtui (x,) = 1, k = 1, . .，满足n, Iti, i = 1，..，意为c。ファジィ分率决定,因此晦,μh(κ)相关的优化问题:minσtk (eq,) ii xh - v1 ll z (il il l2ノル集团)解开。这里，优化砺i= 1， .., c;k;1, . .，n，和Vi, i= 1，..，·c，然后进行。作为限制条件，使得晦表示模糊分割，0≤晦≤Lfor a1141e;σ,uilt = 1, for all k,征收。另外，m是使m > 1的指数尺。bezdek
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引用次数: 2
Model-based clustering 基于模型的聚类
Time Series Clustering and Classification
Pub Date : 2019-03-19 DOI: 10.1201/9780429058264-7
E. Maharaj, P. D’Urso, Jorge Caiado
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引用次数: 0
Time series features and models 时间序列特征和模型
Time Series Clustering and Classification
Pub Date : 2019-03-19 DOI: 10.1201/9780429058264-2
E. Maharaj, P. D’Urso, Jorge Caiado
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引用次数: 0
Traditional cluster analysis 传统聚类分析
Time Series Clustering and Classification
Pub Date : 2019-03-19 DOI: 10.1201/9780429058264-3
E. Maharaj, P. D’Urso, Jorge Caiado
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引用次数: 0
Feature-based clustering 基于特征聚类
Time Series Clustering and Classification
Pub Date : 2019-03-19 DOI: 10.1201/9780429058264-6
E. Maharaj, P. D’Urso, Jorge Caiado
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引用次数: 0
Software and data sets 软件和数据集
Time Series Clustering and Classification
Pub Date : 2019-03-19 DOI: 10.1201/9780429058264-11
Yi Wang, Nassim Ait Ali Braham, Zhitong Xiong, Chenying Liu, C. Albrecht, Xiaoxiang Zhu
S elf-supervised pretraining bears the potential to generate expressive representations from large-scale Earth observation (EO) data without human annotation. However, most existing pretraining in the field is based on ImageNet or medium-sized, labeled remote sensing (RS) datasets. In this article, we share an unlabeled dataset Self-Supervised Learning for Earth Observa-tion-Sentinel-1/2 ( SSL4EO - S12 ) to assemble a large-scale, global, multimodal, and multiseasonal corpus of satellite imagery. We demonstrate SSL4EO-S12 to succeed in self-supervised pretraining for a set of representative methods: momentum contrast (MoCo), self-distillation with no labels (DINO), masked autoencoders (MAE), and data2vec, and multiple downstream applications, including scene classification, semantic segmentation, and change detection. Our benchmark results prove the effectiveness of SSL4EO-S12 compared to existing datasets. The dataset, related source code, and pretrained models are available at https://github.com/zhu-xlab/ SSL4EO-S12.
自监督预训练具有在没有人工注释的情况下从大规模地球观测(EO)数据中生成表达性表示的潜力。然而，该领域现有的大多数预训练都是基于ImageNet或中型标记遥感(RS)数据集。在本文中，我们分享了一个无标记数据集自监督学习地球观测-哨兵1/2 (SSL4EO - S12)，以组装大规模，全球，多模式和多季节的卫星图像语料库。我们展示了SSL4EO-S12在自监督预训练中取得成功的一组代表性方法:动量对比(MoCo)、无标签自蒸馏(DINO)、蒙面自动编码器(MAE)和data2vec，以及多个下游应用，包括场景分类、语义分割和变化检测。我们的基准测试结果证明了与现有数据集相比，SSL4EO-S12的有效性。数据集、相关源代码和预训练模型可从https://github.com/zhu-xlab/ SSL4EO-S12获得。
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引用次数: 0
Other time series classification approaches 其他时间序列分类方法
Time Series Clustering and Classification
Pub Date : 2019-03-19 DOI: 10.1201/9780429058264-10
E. Maharaj, P. D’Urso, Jorge Caiado
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引用次数: 0
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