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Commit 542f8b1c authored by Davis King's avatar Davis King
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Added a regularization parameter to cca()

parent 3c8db585
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Showing with 43 additions and 24 deletions
dlib/statistics/cca.h
View file @ 542f8b1c
......@@ -17,9 +17,9 @@ namespace dlib
template <
typename T
>
matrix<T,0,1> compute_correlations (
const matrix<T>& L,
const matrix<T>& R
matrix<typename T::type,0,1> compute_correlations (
const matrix_exp<T>& L,
const matrix_exp<T>& R
)
{
DLIB_ASSERT( L.size() > 0 && R.size() > 0 && L.nr() == R.nr(),
......@@ -31,7 +31,8 @@ namespace dlib
<< "\n\t R.nr(): " << R.nr()
);
matrix<T> A, B, C;
typedef typename T::type type;
matrix<type> A, B, C;
A = diag(trans(R)*L);
B = sqrt(diag(trans(L)*L));
C = sqrt(diag(trans(R)*R));
......@@ -53,7 +54,8 @@ namespace dlib
unsigned long num_correlations,
unsigned long extra_rank,
unsigned long q,
unsigned long num_output_correlations
unsigned long num_output_correlations,
double regularization
)
{
matrix<T> Ul, Vl;
......@@ -70,8 +72,8 @@ namespace dlib
// Zero out singular values that are essentially zero so they don't cause numerical
// difficulties in the code below.
const double eps = std::numeric_limits<T>::epsilon()*std::max(max(Dr),max(Dl))*100;
Dl = round_zeros(Dl,eps);
Dr = round_zeros(Dr,eps);
Dl = round_zeros(Dl+regularization,eps);
Dr = round_zeros(Dr+regularization,eps);
// This matrix is really small so we can do a normal full SVD on it. Note that we
// also throw away the columns of Ul and Ur corresponding to zero singular values.
......@@ -105,13 +107,16 @@ namespace dlib
matrix<T>& Rtrans,
unsigned long num_correlations,
unsigned long extra_rank = 5,
unsigned long q = 2
unsigned long q = 2,
double regularization = 0
)
{
DLIB_ASSERT( num_correlations > 0 && L.size() > 0 && R.size() > 0 && L.nr() == R.nr(),
DLIB_ASSERT( num_correlations > 0 && L.size() > 0 && R.size() > 0 && L.nr() == R.nr() &&
regularization >= 0,
"\t matrix cca()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t num_correlations: " << num_correlations
<< "\n\t regularization: " << regularization
<< "\n\t L.size(): " << L.size()
<< "\n\t R.size(): " << R.size()
<< "\n\t L.nr(): " << L.nr()
......@@ -120,7 +125,7 @@ namespace dlib
using std::min;
const unsigned long n = min(num_correlations, (unsigned long)min(R.nr(),min(L.nc(), R.nc())));
return impl_cca(L,R,Ltrans, Rtrans, num_correlations, extra_rank, q, n);
return impl_cca(L,R,Ltrans, Rtrans, num_correlations, extra_rank, q, n, regularization);
}
// ----------------------------------------------------------------------------------------
......@@ -133,14 +138,17 @@ namespace dlib
matrix<T>& Rtrans,
unsigned long num_correlations,
unsigned long extra_rank = 5,
unsigned long q = 2
unsigned long q = 2,
double regularization = 0
)
{
DLIB_ASSERT( num_correlations > 0 && L.size() == R.size() &&
max_index_plus_one(L) > 0 && max_index_plus_one(R) > 0,
max_index_plus_one(L) > 0 && max_index_plus_one(R) > 0 &&
regularization >= 0,
"\t matrix cca()"
<< "\n\t Invalid inputs were given to this function."
<< "\n\t num_correlations: " << num_correlations
<< "\n\t regularization: " << regularization
<< "\n\t L.size(): " << L.size()
<< "\n\t R.size(): " << R.size()
<< "\n\t max_index_plus_one(L): " << max_index_plus_one(L)
......@@ -150,7 +158,7 @@ namespace dlib
using std::min;
const unsigned long n = min(max_index_plus_one(L), max_index_plus_one(R));
const unsigned long num_output_correlations = min(num_correlations, std::min<unsigned long>(R.size(),n));
return impl_cca(L,R,Ltrans, Rtrans, num_correlations, extra_rank, q, num_output_correlations);
return impl_cca(L,R,Ltrans, Rtrans, num_correlations, extra_rank, q, num_output_correlations, regularization);
}
// ----------------------------------------------------------------------------------------
......
dlib/statistics/cca_abstract.h
View file @ 542f8b1c
......@@ -14,9 +14,9 @@ namespace dlib
template <
typename T
>
matrix<T,0,1> compute_correlations (
const matrix<T>& L,
const matrix<T>& R
matrix<typename T::type,0,1> compute_correlations (
const matrix_exp<T>& L,
const matrix_exp<T>& R
);
/*!
requires
......@@ -47,7 +47,8 @@ namespace dlib
matrix<T>& Rtrans,
unsigned long num_correlations,
unsigned long extra_rank = 5,
unsigned long q = 2
unsigned long q = 2,
double regularization = 0
);
/*!
requires
......@@ -55,6 +56,7 @@ namespace dlib
- L.size() > 0
- R.size() > 0
- L.nr() == R.nr()
- regularization >= 0
ensures
- This function performs a canonical correlation analysis between the row
vectors in L and R. That is, it finds two transformation matrices, Ltrans
......@@ -83,11 +85,16 @@ namespace dlib
problems.
- returns an estimate of compute_correlations(L*#Ltrans, R*#Rtrans). The
returned vector should exactly match the output of compute_correlations()
when the reduced rank approximation to L and R is accurate. However, when L
and/or R are higher rank than num_correlations+extra_rank the return value of
this function will deviate from compute_correlations(L*#Ltrans, R*#Rtrans).
This deviation can be used to check if the reduced rank approximation is
working or you need to increase extra_rank.
when the reduced rank approximation to L and R is accurate and regularization
is set to 0. However, if this is not the case then the return value of this
function will deviate from compute_correlations(L*#Ltrans, R*#Rtrans). This
deviation can be used to check if the reduced rank approximation is working
or you need to increase extra_rank.
- This function performs the ridge regression version of Canonical Correlation
Analysis when regularization is set to a value > 0. In particular, larger
values indicate the solution should be more heavily regularized. This can be
useful when the dimensionality of the data is larger than the number of
samples.
- A good discussion of CCA can be found in the paper "Canonical Correlation
Analysis" by David Weenink. In particular, this function is implemented
using equations 29 and 30 from his paper. We also use the idea of doing CCA
......@@ -109,7 +116,8 @@ namespace dlib
matrix<T>& Rtrans,
unsigned long num_correlations,
unsigned long extra_rank = 5,
unsigned long q = 2
unsigned long q = 2,
double regularization = 0
);
/*!
requires
......@@ -119,6 +127,7 @@ namespace dlib
(i.e. L and R can't represent empty matrices)
- L and R must contain sparse vectors (see the top of dlib/svm/sparse_vector_abstract.h
for a definition of sparse vector)
- regularization >= 0
ensures
- This is just an overload of the cca() function defined above. Except in this
case we take a sparse representation of the input L and R matrices rather than
......@@ -144,7 +153,8 @@ namespace dlib
matrix<T>& Rtrans,
unsigned long num_correlations,
unsigned long extra_rank = 5,
unsigned long q = 2
unsigned long q = 2,
double regularization = 0
);
/*!
requires
......@@ -154,6 +164,7 @@ namespace dlib
(i.e. L and R can't represent empty matrices)
- L and R must contain sparse vectors (see the top of dlib/svm/sparse_vector_abstract.h
for a definition of sparse vector)
- regularization >= 0
ensures
- returns cca(L.to_std_vector(), R.to_std_vector(), Ltrans, Rtrans, num_correlations, extra_rank, q)
(i.e. this is just a convenience function for calling the cca() routine when
......
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