PPT-Dimensionality reduction: feature extraction & feature

Author : danika-pritchard | Published Date : 2017-07-30

Principle Component Analysis Why Dimensionality Reduction It becomes more difficult to extract meaningful conclusions from a data set as data dimensionality increasesD

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Dimensionality reduction: feature extraction & feature: Transcript

Principle Component Analysis Why Dimensionality Reduction It becomes more difficult to extract meaningful conclusions from a data set as data dimensionality increasesD L Donoho Curse of dimensionality. Computer Graphics Course. June 2013. What is high dimensional data?. Images. Videos. Documents. Most data, actually!. What is high dimensional data?. Images – dimension 3·X·Y. Videos – dimension of image * number of frames. M . Zubair. . Rafique. Muhammad . Khurram. Khan. Khaled. . Alghathbar. Muddassar. . Farooq. . The 8th FTRA International Conference on . Synonyms Merge Feature Reduction E. M Saad , M H Awadalla and A F Alajmi Communication & Electronics Dept., Faculty of Engineering, Helwan UniversityEgypt AbstractFeature reduction is an importantpro Can you detect an abrupt change in this picture?. Ludmila. I . Kuncheva. School of Computer Science. Bangor University. Answer – at the end. Plan. Zeno says there is no such thing as change.... If change exists, is it a good thing?. CISC 5800. Professor Daniel Leeds. The benefits of extra dimensions. Finds existing complex separations between classes. 2. The risks of too-many dimensions. 3. High dimensions with kernels over-fit the outlier data. electroencephalographic records . using . EEGFrame . framework. Alan Jović, Lea Suć, Nikola Bogunović. Faculty of Electrical Engineering and Computing, University of Zagreb. Department of Electronics, Microelectronics, Computer and Intelligent Systems. Corpus. Tool. Martin Weisser. Research . Center. for Linguistics & Applied Linguistics. Guangdong University of Foreign Studies. weissermar@gmail.com. Outline. Genesis of the Tool. Feature . Overview. CISC 5800. Professor Daniel Leeds. The benefits of extra dimensions. Finds existing complex separations between classes. 2. The risks of too-many dimensions. 3. High dimensions with kernels over-fit the outlier data. Devansh Arpit. Motivation. Abundance of data. Required storage space explodes!. Images. Documents. Videos. Motivation. Speedup Algorithms. Motivation. Dimensionality reduction for noise filtering. Vector Representation. k. Ramachandra . murthy. Why Dimensionality Reduction. ?. It . is so easy and convenient to collect . data. Data is not collected only for data mining. Data . accumulates in an unprecedented speed. Data pre-processing . Gaussian Distribution. variance. Standard deviation. Statistical representation . and . independence. of random variables. Probability density can be not Gaussian. Variables can be dependent. problems. nouns It is also used in this paper Many other representations have been found which behave better for some special purposes For example conceptual features represent meaning of the original documents BIOINFORMATICS: DEFINATIONS. http://www.ittc.ku.edu/bioinfo_seminar/images/wheel.gif. WHY WE SELECTED THESE PAPERS? . What is Gene Expression?. It is the process by which information from a gene is used in the synthesis of a functional gene product. Finge sing Ridges and Valleys Paramvir Singh * Department of Computer Engineering Punjabi University Patiala, India Dr. Lakhwinder Kaur Department of Computer Engineering Punjabi University Patiala,

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