PPT-HDLSS
Author : alexa-scheidler | Published Date : 2016-11-29
Discrimination Simple Solution Mean Difference Centroid Method Recall not classically recommended Usually no better than FLD Sometimes worse But avoids estimation
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HDLSS: Transcript
Discrimination Simple Solution Mean Difference Centroid Method Recall not classically recommended Usually no better than FLD Sometimes worse But avoids estimation of covariance Means are very stable. Flexibility. From. Kernel . Embedding. Idea. HDLSS. . Asymptotics. & Kernel Methods. Recall . Flexibility. From. Kernel . Embedding. Idea. HDLSS. . Asymptotics. & Kernel Methods. Recall . . (10 Minute Talks. ). Too many undetermined. Please help fill in.. Return to Big Picture. Main statistical goals of OODA:. Understanding population structure. Low . dim. ’. al. Projections, PCA . Please Send Title:. . Seoyoon. . Cho. . Sumit. . Kar. Jose . Sanchez. Yue . Pan. David . Bang. . Dhruv. . Patel. Wei . Gu. . Bohan. . Li. . Siqi. . Xiang. . Nicolas . Wolczynski. . Mingyi. Please Send Title:. Wei Gu. HDLSS. . Asymptotics. : Simple Paradoxes. For . dimensional . Standard Normal. . dist’n. :. Where are the Data?. Near Peak of Density?. Thanks to: psycnet.apa.org. & PCA Consist.:. John Kent example:. Can only say: . . . . not deterministic. Conclude: For . Geo. . Rep’n. . need . Additional Condition. Reasonable Approach: . Mixing Conditions. Object Oriented Data Analysis. J. S. Marron. Dept. of Statistics and Operations Research. University of North Carolina. Support Vector Machines. Motivation:. Find a linear method that . “. works well.
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