Through SDCCA latent space for multiview learning is determined by incorporating similarities and complementary information from multiple views with cross correlation analysis.
Class SDCCA(scale = True, k = 2)Parameters:
-
scale : boolean, default = True, Whether to scale the data or not.
-
k : int, default = 2, Number of neighbours to consider in K-Means Algorithm.
Attributes:
- x : first data set of dimension n x p with n samples and p features.
- y : second data set of dimension n x q with n samples and q features.
- n : number of samples in datasets
- p : number of features in x dataset
- q : number of features in y dataser
- wx , wy : final projection vectors of two views of p x p and q x q respectively
fit(self, x, y)Fit the model from the data in x and the labels in y and finds the projection vectors
Parameters
- x : array-like, shape (n x p) Training vector, where n is the number of samples, and p is the number of features.
- y : array-like, shape (n x q) Training vector, where n is the number of samples, and q is the number of features.
- wx , wy : Projection vectors
fit_transform(self, x, y)Applies the projection vectors on the dataset
Parameters
- x : array-like, shape (n x p) Training vector, where n is the number of samples, and p is the number of features.
- y : array-like, shape (n x q) Training vector, where n is the number of samples, and q is the number of features.
x_new , y_new : Projected views Input data transformed by the projected vectors
Example:1
from SDCCA import SDCCA
x = np.array([[0., 0., 1.], [1.,0.,0.], [2.,2.,2.], [3.,5.,4.]])
y = np.array([[0.1, 0.2], [0.9, 1.1], [6.2, 5.9], [11.9, 12.3]])
sdcca = SDCCA(scale = False)
wx,wy = sdcca.fit (x, y)
print(wx)
"""
array([[-28.4951993 0.2682586 -0.975527 ]
[-42.1618415 -0.5543857 0.2679151]
[-35.6725722 0.3283515 0.5998168]])
"""
print(wy)
"""
array([[257.9830053 -0.0865349]
[262.5667474 0.0852448]])
"""Example:2
from SDCCA import SDCCA
x = np.array([[1., 2.1, 1.], [1., 2.1, 0.5], [2.,2.,2.], [3.,5.,4.]])
y = np.array([[0.1, 4.2], [0.9, 2.1], [7.2, 5.9], [12.9, 1.3]])
sdcca = SDCCA(k=4)
x_new, y_new = sdcca.fit_transform(x,y)
print(x_new)
"""
[[ -5.8380473 -7.4261078 -6.2538804]
[ -5.823948 -7.4249809 -6.2690812]
[ -12.4623148 -13.7376682 -12.9121544]
[ -18.0069253 -21.6219808 -18.9701994]]
"""
print(y_new)
"""
[[ 3.896946 12.9154699]
[ -1.4534688 8.8647358]
[ -22.7799413 38.134078 ]
[ -50.2993919 40.4397741]]
"""