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Canonical Correlation Analysis: An overview with application to learning methods By David R. Hardoon, Sandor Szedmak, John Shawe-Taylor School of Electronics and Computer Science, University of Southampton Published in Neural Computaion, 2004. Presented by: Shankar Bhargav.

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presented by shankar bhargav

Canonical Correlation Analysis: An overview with application to learning methodsBy David R. Hardoon, Sandor Szedmak, John Shawe-TaylorSchool of Electronics and Computer Science, University of SouthamptonPublished in Neural Computaion, 2004

Presented by:

Shankar Bhargav

canonical correlation analysis
Canonical Correlation Analysis
  • Measuring the linear relationship between two multi dimensional variables
  • Finding two sets of basis vectors such that the correlation between the projections of the variables onto these basis vectors is maximized
  • Determine Correlation Coefficients
canonical correlation analysis3
Canonical Correlation Analysis
  • More than one canonical correlations will be found each corresponding to a different set of basis vectors/Canonical variates
  • Correlations between successively extracted canonical variates are smaller and smaller
  • Correlation coefficients : Proportion of correlation between the canonical variates accounted for by the particular variable.
differences with correlation
Differences with Correlation
  • Not dependent on the coordinate system of variables
  • Finds direction that yield maximum correlations
Find basis vectors for two sets of variables x, y such that the correlations between the projections of the variables onto these basis vector

Sx = (x.wx) and Sy = (y.wy)

ρ = E[Sx Sy ]

√ E[Sx2] E[Sy2]

ρ = E[(xT wx yT wy)]

√E[(xT wx xT wx) ] E[(yT wy yT wy)]

ρ = max wx wy E[wxTxyT wy]

√E[wxTx xT wx ] E[wyT yyT wy]

ρ = max wx wy wxTCxywy

√ wxTCxxwx wyTCyywy

Solving this

with constraint wxTCxxwx=1


Cxx-1CxyCyy-1Cyx wx = ρ2 wx

Cyy-1CyxCxx-1Cxy wy= ρ2 wy

Cxy wy = ρλxCxx wx

Cyx wx = ρλy Cyywy

λx=λy-1= wyTCyywy


cca in matlab
CCA in Matlab

[ A, B, r, U, V ] = canoncorr(x, y)

x, y : set of variables in the form of matrices

  • Each row is an observation
  • Each column is an attribute/feature

A, B: Matrices containing the correlation coefficient

r : Column matrix containing the canonical correlations (Successively decreasing)

U, V: Canonical variates/basis vectors for A,B respectively

interpretation of cca
Interpretation of CCA
  • Correlation coefficient represents unique contribution of each variable to relation
  • Multicollinearity may obscure relationships
  • Factor Loading : Correlations between the canonical variates (basis vector) and the variables in each set
  • Proportion of variance explained by the canonical variates can be inferred by factor loading
redundancy calculation
Redundancy Calculation
  • Redundancy left =[ ∑ (loadingsleft2)/p]*Rc2
  • Redundancy right =[ ∑ (loadingsright2)/q]*Rc2

p – Number of variable in the first (left) set of variables

q – Number of variable in the second (right) set of variables

Rc2 – Respective squared canonical correlation

Since successively extracted roots are uncorrelated we can sum the redundancies across all correlations to get a single index of redundancy.

  • Kernel CCA can be used to find non linear relationships between multi variates
  • Two views of the same semantic object to extract the representation of the semantics
    • Speaker Recognition – Audio and Lip movement
    • Image retrieval – Image features (HSV, Texture) and Associated text
use of kcca in cross modal retrieval
Use of KCCA in cross-modal retrieval
  • 400 records of JPEG images for each class with associated text and a total of 3 classes
  • Data was split randomly into 2 parts for training and test
  • Features
    • Image – HSV Color, Gabor texture
    • Text – Term frequencies
  • Results were taken for an average of 10 runs
cross modal retrieval
Cross-modal retrieval
  • Content based retrieval: Retrieve images in the same class
  • Tested with 10 and 30 images sets
    • where countjk = 1 if the image k in the set is of the same label as the text query present in the set, else countjk = 0.
mate based retrieval
Mate based retrieval
  • Match the exact image among the selected retrieved images
  • Tested with 10 and 30 images sets
    • where countj = 1 if the exact matching image was present in the set else it is 0
  • The good
    • Good explanation of CCA and KCCA
    • Innovative use of KCCA in image retrieval application
  • The bad
    • The data set and the number of classes used were small
    • The image set size is not taken into account while calculating accuracy in Mate based retrieval
    • Could have done cross-validation tests
limitations and assumptions of cca
Limitations and Assumptions of CCA
  • At least 40 to 60 times as many cases as variables is recommended to get relliable estimates for two roots– BarciKowski &Stevens(1986)
  • Outliers can greatly affect the canonical correlation
  • Variables in two sets should not be completely redundant