PCA projects

Aug 14, 2014

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PCA projects.

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PCA projects

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PCA projects

PCA

PCA. Mechanics. Component. The component is a linear combination of the original variables The variance of y 1 is maximized subject to the constraint that the sum of the squared weights equals 1 Using standardized data

418 views • 18 slides

Dimension Reduction - PCA

Dimension Reduction - PCA . P rinciple C omponent A nalysis. The Goals. Reduce the number of dimensions of a data set. Capture the maximum information present in the initial data set. Minimize the error between the original data set and the reduced dimensional data set.

767 views • 49 slides

SMART PCA

SMART PCA. SUMMARY. Smart Airport The Passenger The Comfort PCA System Smart PCA. Smart Airport. Airports are, like living beings, in constant movement, adapting their facilities and creating new services according to the needs of their passengers.

841 views • 25 slides

PCA + SVD

PCA + SVD. Motivation – Shape Matching. There are tagged feature points in both sets that are matched by the user. What is the best transformation that aligns the unicorn with the lion?. Motivation – Shape Matching. Regard the shapes as sets of points and try to “match” these sets

678 views • 31 slides

ICA and PCA

ICA and PCA. 學生：周節教授：王聖智教授. Outline. Introduction PCA ICA Reference. Introduction. Why are these methods ? A: For computational and conceptual simplicity. And it is more convenient to analysis. What are these methods ?

785 views • 38 slides

Triggers for PCA

Triggers for PCA. Financial Regulation Seminar June 9, 2008. Thorvald Grung Moe Norges Bank (Central Bank of Norway). The views presented are mine and should not be associated with Norges Bank. Outline. Background Key features of PCA PCA in Europe? Design of PCA triggers

854 views • 61 slides

OCR using PCA

OCR using PCA. Ohad klausner. Introduction. What is OCR?? Optical character recognition. What is PCA???? principal components analysis reducing dimnesionalty in a dataset retaining characteristics of the dataset. Why PCA?. Appearance based recognition Suited for OCR

216 views • 5 slides

Is PCA enough ?

Is PCA enough ?. Irena Váňová. A 1. A 2. A 3. Dot product. B. for i=1...N if then end end. *. *. *. *. *. o. o. *. *. o. *. o. *. o. o. Perceptron algorithm. … labels of classes. repeat until no sample is misclassified.

286 views • 18 slides

PCA Data

PCA Data. PCA Data minus mean. Eigenvectors. Compressed Data. Spectral Data. Eigenvectors. Fit Error – 2 Eigenfunctions. Singular Value Decomposition (SVD). A matrix A mxn with m rows and n columns can be decomposed into. A = USV T.

298 views • 15 slides

Robust PCA

Robust PCA. Robust PCA 3: Spherical PCA. Robust PCA. Spherical PCA Problem : Magnification of High Freq. Coeff ’ s Solution : Elliptical Analysis Background ( Univariate ): MAD = Median Absolute Deviation MAD =

964 views • 64 slides

a lkfos PCA

a lkfos PCA. SPH 247 Statistical Analysis of Laboratory Data. > library( ISwR ) > data( alkfos ) > names( alkfos ) [1] " grp " "c0" "c3" "c6" "c9" "c12" "c18" " c24“ > summary( alkfos ) grp c0 c3 c6

172 views • 8 slides

PCA Channel

PCA Channel. Student: Fangming JI u4082259 Supervisor: Professor Tom Geoden. Organization of the Presentation. PCA and problems PCA channel idea Use the channel for automatic classification Channel Corrected Channel Conclusion Future work. Principle Component Analysis. A statistic tool

417 views • 20 slides

PCA Extension

PCA Extension. By Jonash. Outline. Robust PCA Generalized PCA Clustering points on a line Clustering lines on a plane Clustering hyperplanes in a space. Robust PCA. Rrbust Principal Component Analysis for Computer Vision Fernando De la Torre Mochael J. Black CS, Brown University.

674 views • 45 slides

Robust PCA

Robust PCA. Principal Component Analysis (PCA). PCA (Pearson, 1901) 最优线性重构 PCA (Hotelling, 1933) 最优方差投影方向 Probabilistic PCA (1999) 高斯隐变量模型 Robust PCA (2009). Why use Robust PCA?. Solve the problem with spike noise with high magnitude instead of Gaussian distributed noise. Main Problem.

1.93k views • 38 slides

Dimension Reduction - PCA

Dimension Reduction - PCA. P rinciple C omponent A nalysis. The Goals. Reduce the number of dimensions of a data set. Capture the maximum information present in the initial data set. Minimize the error between the original data set and the reduced dimensional data set.

547 views • 49 slides

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