Patent ID: 7996342

Claim:
In a computer system, a method for supervised dimensionality reduction for predicting data for at least one of end-to-end connectivity in a wired and wireless sensor network, customer response to advertisement, and functional magnetic resonance imaging (fMRI) data analysis, the method comprising: receiving input data for at least one of the end-to-end connectivity in a wired and wireless sensor network, the customer response to advertisement, and the functional magnetic resonance imaging (fMRI) data analysis, the input data in the form of a data matrix X of size N×D, wherein N is a number of samples, D is a dimensionality, a vector Y of size N×1, hidden variables U of a number K, a data type of the matrix X and the vector Y, and a trade-off constant alpha; selecting loss functions in the form of Lx(X,UV) and Ly(Y,UW) appropriate for the type of data in the matrix X and the vector Y, where U, V and W are matrices; selecting corresponding sets of update rules RU, RV and RW for updating the matrices U,V and W; learning U, V and W that provide a minimum total loss L(U,V,W)=Lx(X,UV)+alpha*Ly(Y,UW), including: defining a threshold epsilon; initializing the matrices U, V and W to random matrices; for epsilon less than or equal to L-L(U,V,W), iteratively performing: fixing matrices V and W; updating matrix U via rules RU, wherein U=RU(U); fixing matrices U and W; updating matrix V via rules RV, wherein V=RV(V); fixing matrices U and V; updating matrix W via rules RW, wherein W=RW(W); and returning matrices U, V and W.