PDF-Subspace Clustering for Sequential Data Stephen Tierney and Junbin Gao School of Computing

eduau Yi Guo Division of Computational Informatics CSIRO North Ryde NSW 2113 Australia yiguocsiroau Abstract We propose Ordered Subspace Clustering OSC to seg ment data drawn from a sequentially ordered union of sub spaces Current subspace clustering. M. Soltanolkotabi E.Elhamifar E.J. Candes. 报告. 人：万晟、元玉慧. 、. 张. 驰. 昱. 信息科学与技术学院. 智. 能科学系. 1. Main Contribution. Existing work. Subspace Clustering. Brendan and Yifang . April . 21 . 2015. Pre-knowledge. We define a set A, and we find the element that minimizes the error. We can think of as a sample of . Where is the point in C closest to X. . Asymptotics. Yining Wang. , Jun . zhu. Carnegie Mellon University. Tsinghua University. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. 1 . (. Elhamifar. Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . W. of a vector space . V. . Recall:. Definition: . The examples we have seen so far originated from considering the span of the column vectors of a matrix . A. , or the solution set of the equation. Yining Wang. , Yu-Xiang Wang, . Aarti. Singh. Machine Learning Department. Carnegie . mellon. university. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. via Subspace Clustering. Ruizhen. Hu . Lubin. Fan . Ligang. Liu. Co-segmentation. Hu et al.. Co-Segmentation of 3D Shapes via Subspace Clustering. 2. Input. Co-segmentation. Hu et al.. René Vidal. Center for Imaging Science. Institute for Computational Medicine. Johns Hopkins University. Data segmentation and clustering. Given a set of points, separate them into multiple groups. Discriminative methods: learn boundary. What is clustering?. Why would we want to cluster?. How would you determine clusters?. How can you do this efficiently?. K-means Clustering. Strengths. Simple iterative method. User provides “K”. ROOM. By Connor Ericson and Jennifer Akosa. Preliminary. What . is . Ivy Day. ? . The . anniversary of the death of  the 19th century Irish . politician Charles Parnell . regarded for his achievement . Lecture outline. Distance/Similarity between data objects. Data objects as geometric data points. Clustering problems and algorithms . K-means. K-median. K-center. What is clustering?. A . grouping. of data objects such that the objects . 1. Mark Stamp. K-Means for Malware Classification. Clustering Applications. 2. Chinmayee. . Annachhatre. Mark Stamp. Quest for the Holy . Grail. Holy Grail of malware research is to detect previously unseen malware. Produces a set of . nested clusters . organized as a hierarchical tree. Can be visualized as a . dendrogram. A . tree-like . diagram that records the sequences of merges or splits. Strengths of Hierarchical Clustering. Log. 2. transformation. Row centering and normalization. Filtering. Log. 2. Transformation. Log. 2. -transformation makes sure that the noise is independent of the mean and similar differences have the same meaning along the dynamic range of the values..