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An improved group lasso is proposed for simultaneous cancer classification and gene selection. A new criterion is firstly proposed to evaluate the individual gene importance by using the conditional mutual information. Then the weights with biological explanation are constructed and the improved group lasso is presented. A blockwise descent algorithm for solving the proposed model is also developed. The experimental results on lung cancer and prostate cancer data sets demonstrate that the proposed method can effectively perform classification and gene selection.

This paper presents a local tone mapping method to render high dynamic range images on conventional displays. We adaptively stretch contrast in local regions to reproduce local contrast. In order to avoid halos, we use bilateral filtering to smooth the image prior to the contrast stretching operation. Our method is fast and easy to use, and the experiment results show that the technique can produce good results on a variety of high dynamic range images