kernel

# kernel - Kernel Methods Kernel Methods 2 Simple Idea of...

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Unformatted text preview: Kernel Methods Kernel Methods 2 Simple Idea of Data Fitting x Given ( x i ,y i ) b i=1,,n b x i is of dimension d x Find the best linear function w (hyperplane) that fits the data x Two scenarios b y: real, regression b y: {-1,1}, classification x Two cases b n>d, regression, least square b n<d, ridge regression x New sample: x , < x,w> : best fit (regression), best decision (classification) 3 Primary and Dual x There are two ways to formulate the problem: b Primary b Dual x Both provide deep insight into the problem x Primary is more traditional x Dual leads to newer techniques in SVM and kernel methods 4 Regression > =< = = =- =---- = -- =-- y X X X x y X X X w y X Xw X Xw y X w Xw y Xw y Xw y Xw y w w W W T T T T T T T T T i j j ij o i y d d w x w y 1 1 2 ) ( , ) ( ) ( ) ( ) ( ) ( ) ( min arg ) ( min arg ) d T n T T T n T d T d o y y y x x w w w = = = = x x x X y x w M L L L 2 1 2 1 1 1 ] , , , [ , ] , , , 1 [ , ] , , , [ 5 x X is a n (sample size) by d (dimension of data) matrix x w combines the columns of X to best approximate y b Combine features (FICA, income, etc.) to decisions (loan) x H projects y onto the space spanned by columns of X b Simplify the decisions to fit the features y X X X X y X X X X Hy Xw y T T T T 1 1 ) ( ) (-- = = = = ) Graphical Interpretation n d X= FICA Income 6 Problem #1 x n=d, exact solution x n>d, least square, (most likely scenarios) x When n < d, there are not enough constraints to determine coefficients w uniquely n d X= W 7 Problem #2 x If different attributes are highly correlated (income and FICA) x The columns become dependent x Coefficients are then poorly determined with high variance b E.g., large positive coefficient on one can be canceled by a similarly large negative coefficient on its correlated cousin b Size constraint is helpful b Caveat: constraint is problem dependent 8 Ridge Regression x Similar to regularization > + =< + = + = + = = +-- = +-- +-- = +-- =-- y X I X X x y X I X X w w I X X y X w Xw X y X w Xw y X w w w Xw y Xw y w w Xw y Xw y w w W W T T T T ridge T T T T T T T T T ridge i j j j j ij o i ridge y d d w w x w y 1 1 2 2 ) ( , ) ( ) ( ) ( ) ( ) ( ) ( ) ( min arg ) ( min arg 9 Ugly Math y u u y U I U y U I V V V U y U V I V U y U V I U U V U y X I X X X Xw y y X I X X w T i i i i i T T T T T T T T T T T T T T T T T T T T T T ridge T T ridge V V V V V V V + = + = + = + = + = + = = + = -------------- 2 2 1 1 1 1 1 1 1 1 1 1 1 ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) T V U X = 10 How to Decipher This x Red: best estimate (y hat) is composed of columns of U (basis features, recall U and X have the same column space) x Green: how these basis columns are weighed x Blue: projection of target (y) onto these columns x Together: representing y in a body-fitted coordinate system ( u i ) y u u y T i i i i i + = 2 2 ) 11...
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## This note was uploaded on 08/06/2008 for the course CS 290I taught by Professor Wang during the Spring '07 term at UCSB.

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kernel - Kernel Methods Kernel Methods 2 Simple Idea of...

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