  We propose a method for finding alternate features missing in the Lasso
optimal solution. In ordinary Lasso problem, one global optimum is obtained and
the resulting features are interpreted as task-relevant features. However, this
can overlook possibly relevant features not selected by the Lasso. With the
proposed method, we can provide not only the Lasso optimal solution but also
possible alternate features to the Lasso solution. We show that such alternate
features can be computed efficiently by avoiding redundant computations. We
also demonstrate how the proposed method works in the 20 newsgroup data, which
shows that reasonable features are found as alternate features.
