PDF-Sparse Manifold Clustering and Embedding Ehsan Elhamif

jhuedu Ren e Vidal Center for Imaging Science Johns Hopkins University rvidalcisjhuedu Abstract We propose an algorithm called Sparse Manifold Clustering and Embedding SMCE for simultaneous clustering and dimensionality reduction of data lying in mul. Such matrices has several attractive properties they support algorithms with low computational complexity and make it easy to perform in cremental updates to signals We discuss applications to several areas including compressive sensing data stream Alexandr. . Andoni. . (Simons Institute). Robert . Krauthgamer. . (. Weizmann. . Institute). Ilya Razenshteyn . (CSAIL MIT). 1. Sketching. Compress a massive object to a . small. . sketch. Rich theories: . James McQueen – UW Department of Statistics . About Me. 4. th. year PhD student working with Marina . Meila. Work in Machine Learning focus on Manifold Learning. Worked at Amazon . on Personalization . One of these things is not like the other…. spectral clustering (a la Ng-Jordan-Weiss). data. similarity graph. edges have weights . w. (. i. ,. j. ). e.g.. the . Laplacian. diagonal matrix . D. Normalized . Yining Wang. , Yu-Xiang Wang, . Aarti. Singh. Machine Learning Department. Carnegie . mellon. university. 1. Subspace Clustering. 2. Subspace Clustering Applications. Motion Trajectories tracking. 1. René Vidal. Center for Imaging Science. Institute for Computational Medicine. Johns Hopkins University. Manifold Clustering with Applications to Computer Vision and Diffusion Imaging. René Vidal. Center for Imaging Science. issue in . computing a representative simplicial complex. . Mapper does . not place any conditions on the clustering . algorithm. Thus . any domain-specific clustering algorithm can . be used.. We . 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”. Unsupervised . learning. Seeks to organize data . into . “reasonable” . groups. Often based . on some similarity (or distance) measure defined over data . elements. Quantitative characterization may include. 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 . M. Ehsan Hoque Assistant Professor Rochester Human-Computer Interaction Group (ROC HCI) University of Rochester Twitter: @ ehsan_hoque NSF CISE CAREER WORKSHOP 2019 My Background Affiliation: Assistant Professor of computer science at the University of Rochester 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. - Preliminary Thermal Analysis. Dan Wilcox. STFC/RAL. March 2020. Summary of Changes . New upstream manifold. Double . conical vessel, including largest allowable taper. Flow divider inner radius increased to 16.5mm, leaving ±5mm for .