US 9,542,654 B2 · Overlapping trace norms for multi-view learning

Abstract

In multi-view learning, optimized prediction matrices are determined for V≧2 views of n objects, and a prediction of a view of an object is generated based on the optimized prediction matrix for that view. An objective is optimized, wherein is a set of parameters including at least the V prediction matrices and a concatenated matrix comprising a concatenation of the prediction matrices, and comprises a sum including at least a loss function for each view, a trace norm of the prediction matrix for each view, and a trace norm of the concatenated matrix. may further include a sparse matrix for each view, with further including an element-wise norm of the sparse matrix for each view. may further include regularization parameters scaling the trace norms of the prediction matrices and the trace norm of the concatenated matrix.

Claims20

Dependent claims

A non-transitory storage medium storing instructions readable and executable by a computer to perform a method for generating predictions by operations including:

determining optimized prediction matrices {Xk}k=1V for multi-view learning of V views where V≧2 by optimizing an objective wherein is a set of parameters including at least the prediction matrices X1, . . . , XV and a matrix X0 comprising a concatenation of the prediction matrices X1, . . . , XV, and function comprises a sum including at least:
λ
0
⁢

X
0

*
+
∑
k
=
1
V
⁢
λ
k
⁢

X
k

*
+
∑
k
=
1
V
⁢
E
k
⁡
(
X
k
+
P
k
⁢
X
0
;
Y
k
)
where {Yk}k=1V are incomplete observation matrices for the V views, PkX0 denotes the sub-matrix of X0 corresponding to prediction matrix Xk, Ek is a cumulative loss summed over the observations of the observation matrix Yk, ∥•∥* denotes the trace norm, and {λk}k=0V are regularization parameters;
and
generating a prediction for view k of an object based on the optimized prediction matrix Xk.

47 more elements

A method comprising:

determining optimized prediction matrices for multi-view learning of V views where V≧2 by optimizing an objective wherein is a set of parameters including at least the prediction matrices for the V views and for a set of objects and an aggregation of the prediction matrices for the V views, and function comprises a sum including at least:
(1) a loss function for each view comparing the prediction matrix for the view with an incomplete observation matrix for the view and
(2) a penalty function for each view computed based on the prediction matrix for the view and
(3) a penalty function computed based on the aggregation of the prediction matrices for the V views;
and
generating a prediction of a view of an object based on the optimized prediction matrix for that view;
wherein the determining and generating operations are performed by an electronic data processing device.

4 more elements

Dependencies are inferred from the claim text.

Overlapping trace norms for multi-view learning

Life of this patent

Overlapping trace norms for multi-view learning

Life of this patent

Abstract

Claims20