We consider the problem of extracting joint and individual signals from multi-view data, that is, data collected different sources on matched samples. While existing methods for decomposition explore single matching by samples, we focus double-matched (matched both samples source features). Our motivating example is miRNA primary tumor normal tissues same subjects; measurements two are thus sub...

Hashing has been drawing increasing attention in the task of large-scale image retrieval owing to its storage and computation efficiency, especially recent asymmetric deep hashing methods. These approaches treat query database an way can take full advantage whole training data. Though it achieved state-of-the-art performance, methods still suffer from large quantization error efficiency problem...

In longitudinal studies, serial dependence of repeated outcomes must be taken into account to make correct inferences on covariate effects. As such, care must be taken in modeling the covariance matrix. However, estimation of the covariance matrix is challenging because there are many parameters in the matrix and the estimated covariance matrix should be positive definite. To overcomes these li...

In this paper we discuss matrix decompositions in the symmetrized max-plus algebra. The max-plus algebra has maximization and addition as basic operations. In contrast to linear algebra many fundamental problems in the max-plus algebra still have to be solved. In this paper we discuss max-algebraic analogues of some basic matrix decompositions from linear algebra. We show that we can use algori...

We present a family of algorithms for computing symmetric rank-revealing VSV decompositions, based on triangular factorization of the matrix. The VSV decomposition consists of a middle symmetric matrix that reveals the numerical rank in having three blocks with small norm, plus an orthogonalmatrix whose columns span approximations to the numerical range and null space. We show that for semi-de ...