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Flexible Metric NN Classification. based on Friedman (1995) David Madigan. Nearest-Neighbor Methods. k -NN assigns an unknown object to the most common class of its k nearest neighbors Choice of k ? (bias-variance tradeoff again) Choice of metric?

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flexible metric nn classification

Flexible Metric NN Classification

based on Friedman (1995)

David Madigan

nearest neighbor methods
Nearest-Neighbor Methods
  • k-NN assigns an unknown object to the most common class of its k nearest neighbors
  • Choice of k? (bias-variance tradeoff again)
  • Choice of metric?
  • Need all the training to be present to classify a new point (“lazy methods”)
  • Surprisingly strong asymptotic results (e.g. no decision rule is more than twice as accurate as 1-NN)
suppose a regression surface looks like this
Suppose a Regression Surface Looks like this:

want this

not this

Flexible-metric NN Methods try to capture this idea…

slide5
FMNN
  • Predictors may not all be equally relevant for classifying a new object
  • Furthermore, this differential relevance may depend on the location of the new object
  • FMNN attempts to model this phenomenon
local relevance
Local Relevance
  • Consider an arbitrary function f on Rp
  • If no values of x are known, have:
  • Suppose xi=z, then:
local relevance cont
Local Relevance cont.
  • The improvement in squared error provided by knowing xi is:
  • I2i(z) reflects the importance of the ith variable on the variation of f(x) at xi=z
local relevance cont1
Local Relevance cont.
  • Now consider an arbitrary point z=(z1,…,zp)
  • The relative importance of xi to the variation of f at x=z is:
  • R2i(z)=0 when f(x) is independent of xi at z
  • R2i(z)=1 when f(x) depends only on xi at z
estimation
Estimation
  • Recall:
on to classification
On To Classification
  • For J-class classification have {yj}, j=1,…,J output variables, yje {0,1}, S yj=1.
  • Can compute:
  • Technical point: need to weight the observations to rectify unequal variances
the machete
The Machete
  • Start with all data points R0
  • Compute
  • Then:
  • Continue until Ri contains K points

M1th order statistic

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