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Added cca()
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Davis King
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@ -8,6 +8,7 @@
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#include "statistics/random_subset_selector.h"
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#include "statistics/random_subset_selector.h"
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#include "statistics/image_feature_sampling.h"
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#include "statistics/image_feature_sampling.h"
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#include "statistics/sammon.h"
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#include "statistics/sammon.h"
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#include "statistics/cca.h"
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#endif // DLIB_STATISTICs_H_
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#endif // DLIB_STATISTICs_H_
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122
dlib/statistics/cca.h
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122
dlib/statistics/cca.h
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// Copyright (C) 2013 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#ifndef DLIB_CCA_h__
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#define DLIB_CCA_h__
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#include "cca_abstract.h"
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#include "../algs.h"
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#include "../matrix.h"
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namespace dlib
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{
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// ----------------------------------------------------------------------------------------
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template <
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typename T
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>
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matrix<T,0,1> compute_correlations (
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const matrix<T>& L,
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const matrix<T>& R
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)
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{
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matrix<T> A, B, C;
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A = diag(trans(R)*L);
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B = sqrt(diag(trans(L)*L));
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C = sqrt(diag(trans(R)*R));
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A = pointwise_multiply(A , reciprocal(pointwise_multiply(B,C)));
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return A;
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}
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// ----------------------------------------------------------------------------------------
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template <
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typename matrix_type,
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typename T
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>
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matrix<T,0,1> impl_cca (
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const matrix_type& L,
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const matrix_type& R,
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matrix<T>& Ltrans,
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matrix<T>& Rtrans,
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unsigned long num_correlations,
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unsigned long extra_rank,
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unsigned long q,
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unsigned long num_output_correlations
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)
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{
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matrix<T> Ul, Vl;
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matrix<T> Ur, Vr;
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matrix<T> U, V;
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matrix<T,0,1> Dr, Dl, D;
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// Note that we add a few more singular vectors in because it helps improve the
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// final results if we run this part with a little higher rank than the final SVD.
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svd_fast(L, Ul, Dl, Vl, num_correlations+extra_rank, q);
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svd_fast(R, Ur, Dr, Vr, num_correlations+extra_rank, q);
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// This matrix is really small so we can do a normal full SVD on it.
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svd3(trans(Ul)*Ur, U, D, V);
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// now throw away extra columns of the transformations. We do this in a way
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// that keeps the directions that have the highest correlations.
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matrix<T,0,1> temp = D;
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rsort_columns(U, temp);
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rsort_columns(V, D);
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U = colm(U, range(0, num_output_correlations-1));
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V = colm(V, range(0, num_output_correlations-1));
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D = rowm(D, range(0, num_output_correlations-1));
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Ltrans = Vl*inv(diagm(Dl))*U;
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Rtrans = Vr*inv(diagm(Dr))*V;
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// Note that the D matrix contains the correlation values for the transformed
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// vectors. However, when the L and R matrices have rank higher than
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// num_correlations+extra_rank then the values in D become only approximate.
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return D;
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}
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// ----------------------------------------------------------------------------------------
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template <typename T>
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matrix<T,0,1> cca (
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const matrix<T>& L,
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const matrix<T>& R,
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matrix<T>& Ltrans,
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matrix<T>& Rtrans,
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unsigned long num_correlations,
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unsigned long extra_rank = 5,
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unsigned long q = 2
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)
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{
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using std::min;
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const unsigned long n = min(num_correlations, (unsigned long)min(R.nr(),min(L.nc(), R.nc())));
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return impl_cca(L,R,Ltrans, Rtrans, num_correlations, extra_rank, q, n);
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}
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// ----------------------------------------------------------------------------------------
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template <typename sparse_vector_type, typename T>
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matrix<T,0,1> cca (
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const std::vector<sparse_vector_type>& L,
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const std::vector<sparse_vector_type>& R,
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matrix<T>& Ltrans,
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matrix<T>& Rtrans,
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unsigned long num_correlations,
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unsigned long extra_rank = 5,
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unsigned long q = 2
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)
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{
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using std::min;
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const unsigned long n = min(max_index_plus_one(L), max_index_plus_one(R));
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const unsigned long num_output_correlations = min(num_correlations, min(R.size(),n));
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return impl_cca(L,R,Ltrans, Rtrans, num_correlations, extra_rank, q, num_output_correlations);
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}
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_CCA_h__
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136
dlib/statistics/cca_abstract.h
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136
dlib/statistics/cca_abstract.h
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// Copyright (C) 2013 Davis E. King (davis@dlib.net)
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// License: Boost Software License See LICENSE.txt for the full license.
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#undef DLIB_CCA_AbSTRACT_H__
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#ifdef DLIB_CCA_AbSTRACT_H__
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#include "../matrix/matrix_la_abstract.h"
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namespace dlib
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{
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// ----------------------------------------------------------------------------------------
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template <
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typename T
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>
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matrix<T,0,1> compute_correlations (
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const matrix<T>& L,
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const matrix<T>& R
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);
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/*!
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requires
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- L.size() > 0
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- R.size() > 0
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- L.nr() == R.nr()
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ensures
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- This function treats L and R as sequences of paired row vectors. It
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then computes the correlation values between the elements of these
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row vectors. In particular, we return a matrix COR such that:
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- COR.size() == L.nc()
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- for all valid i:
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- COR(i) == the correlation coefficient between the following
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sequence of paired numbers: (L(k,i), R(k,i)) for k: 0 <= k < L.nr().
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Therefore, COR(i) is a value between -1 and 1 inclusive where 1
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indicates perfect correlation and -1 perfect anti-correlation.
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!*/
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// ----------------------------------------------------------------------------------------
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template <
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typename T
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>
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matrix<T,0,1> cca (
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const matrix<T>& L,
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const matrix<T>& R,
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matrix<T>& Ltrans,
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matrix<T>& Rtrans,
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unsigned long num_correlations,
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unsigned long extra_rank = 5,
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unsigned long q = 2
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);
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/*!
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requires
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- num_correlations > 0
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- L.size() > 0
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- R.size() > 0
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- L.nr() == R.nr()
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ensures
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- This function performs a canonical correlation analysis between the row
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vectors in L and R. That is, it finds two transformation matrices, Ltrans
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and Rtrans, such that row vectors in the transformed matrices L*Ltrans and
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R*Rtrans are as correlated as possible. That is, we try to find two transforms
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such that the correlation values returned by compute_correlations(L*Ltrans, R*Rtrans)
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would be maximized.
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- Let N == min(num_correlations, min(R.nr(),min(L.nc(),R.nc())))
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(This is the actual number of elements in the transformed vectors.
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Therefore, note that you can't get more outputs than there are rows or
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columns in the input matrices.)
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- #Ltrans.nr() == L.nc()
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- #Ltrans.nc() == N
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- #Rtrans.nr() == R.nc()
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- #Rtrans.nc() == N
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- No centering is applied to the L and R matrices. Therefore, if you want a
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CCA relative to the centered vectors then you must apply centering yourself
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before calling cca().
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- This function works with reduced rank approximations of the L and R matrices.
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This makes it fast when working with large matrices. In particular, we use
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the svd_fast() routine to find reduced rank representations of the input
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matrices by calling it as follows: svd_fast(L, U,D,V, num_correlations+extra_rank, q)
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and similarly for R. This means that you can use the extra_rank and q
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arguments to cca() to influence the accuracy of the reduced rank
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approximation. However, the default values should work fine for most
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problems.
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- returns an estimate of compute_correlations(L*#Ltrans, R*#Rtrans). The
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returned vector should exactly match the output of compute_correlations()
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when the reduced rank approximation to L and R is accurate. However, when L
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and/or R are higher rank than num_correlations+extra_rank the return value of
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this function will deviate from compute_correlations(L*#Ltrans, R*#Rtrans).
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This deviation can be used to check if the reduced rank approximation is
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working or you need to increase extra_rank.
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- A good discussion of CCA can be found in the paper "Canonical Correlation
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Analysis" by David Weenink. In particular, this function is implemented
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using equations 29 and 30 from his paper. We also use the idea of doing CCA
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on a reduced rank approximation of L and R as suggested by Paramveer S.
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Dhillon in his paper "Two Step CCA: A new spectral method for estimating
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vector models of words".
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!*/
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// ----------------------------------------------------------------------------------------
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template <
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typename sparse_vector_type,
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typename T
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>
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matrix<T,0,1> cca (
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const std::vector<sparse_vector_type>& L,
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const std::vector<sparse_vector_type>& R,
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matrix<T>& Ltrans,
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matrix<T>& Rtrans,
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unsigned long num_correlations,
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unsigned long extra_rank = 5,
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unsigned long q = 2
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);
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/*!
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requires
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- num_correlations > 0
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- L.size() == R.size()
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- max_index_plus_one(L) > 0 && max_index_plus_one(R) > 0
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(i.e. L and R can't be a empty matrices)
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ensures
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- This is just an overload of the cca() function defined above. Except in this
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case we take a sparse representation of the input L and R matrices rather than
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dense matrices. Therefore, in this case, we interpret L and R as matrices
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with L.size() rows, where each row is defined by a sparse vector. So this
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function does exactly the same thing as the above cca().
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- Note that you can apply the output transforms to a sparse vector with the
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following code:
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sparse_matrix_vector_multiply(trans(Ltrans), your_sparse_vector)
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!*/
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// ----------------------------------------------------------------------------------------
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}
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#endif // DLIB_CCA_AbSTRACT_H__
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