# Lecture2 - Adaptive Algorithms for PCA PART II Ojas rule is...

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Adaptive Algorithms for PCA PART – II

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Oja’s rule is the basic learning rule for PCA and extracts the first principal component Deflation procedure can be used to estimate the minor eigencomponents Sanger’s rule does an on-line deflation and uses Oja’s rule to estimate the eigencomponents Problems with Sanger’s rule- Strictly speaking, Sanger’s rule is non-local and makes it a little harder for VLSI implementation. Non-local rules are termed as biologically non-plausible! (As engineers, we don’t care very much about this) Sanger’s rule converges slowly. We will see later that many algorithms for PCA converge slowly.
Other Adaptive structures for PCA The first step would be to change the architecture of the network so that the update rules become local. INPUT X(n) WEIGHTS -W LATERAL WEIGHTS - C

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This is the Rubner-Tavan Model. Output vector y is given by Cy Wx y + = x1 x2 w1 w2 c y1 y2 1 2 2 1 1 cy X w y X w y T T + = = C is a lower triangular matrix and this is usually called as
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Lecture2 - Adaptive Algorithms for PCA PART II Ojas rule is...

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