Monday January 30th at 4pm

which is known to be NP-hard. In this talk, by using matrix arguments, we first model MSSC as a so-called 0-1 semidefinite programming (SDP).

We show that our 0-1 SDP model provides an unified framework for several clustering approaches such as normalized k-cut and spectral

clustering. Moreover, the 0-1 SDP model allows us to solve the underlying problem approximately via the relaxed linear and semidefinite programming.

Secondly, we consider the issue of how to extract a feasible solution of the original 0-1 SDP model from the approximate solution of the relaxed SDP problem.

By using principal component analysis, we develop a rounding procedure to construct a feasible partitioning from a solution of the relaxed problem.

In our rounding procedure, we need to solve a K-means clustering problem in

the running time of our rounding procedure can be reduced to

Promising numerical results for bi-clustering based on our new method are reported.