Iteratively Reweighted Least Squares Algorithms for L1-Norm Principal Component Analysis

Principal component analysis (PCA) is often used to reduce the dimension of data by selecting a few orthonormal vectors that explain most of the variance structure of the data. L1 PCA uses the L1 norm to measure error, whereas the conventional PCA uses the L2 norm. For the L1 PCA problem minimizing the fitting error of the reconstructed data, we propose an exact reweighted and an approximate algorithm based on iteratively reweighted least squares. We provide convergence analyses, and compare their performance against benchmark algorithms in the literature. The computational experiment shows that the proposed algorithms consistently perform best

Unblinding the OS to Optimize User-Perceived Flash SSD Latency

In this paper, we present a flash solid-state drive (SSD) optimization that provides hints of SSD internal behaviors, such as device I/O time and buffer activities, to the OS in order to mitigate the impact of I/O completion scheduling delays. The hints enable the OS to make reliable latency predictions of each I/O request so that the OS can make accurate scheduling decisions when to yield or block (busy wait) the CPU, ultimately improving user-perceived I/O performance. This was achieved by implementing latency predictors supported with an SSD I/O behavior tracker within the SSD that tracks I/O behavior at the level of internal resources, such as DRAM buffers or NAND chips. Evaluations with an SSD prototype based on a Xilinx Zynq-7000 FPGA and MLC flash chips showed that our optimizations enabled the OS to mask the scheduling delays without severely impacting system parallelism compared to prior I/O completion methods.We would like to thank the anonymous USENIX HotStorage reviewers. This research was supported by NextGeneration Information Computing Development Program through the National Research Foundation of Korea(NRF) funded by the Ministry of Science, ICT & Future Plannig (2015M 3C 4A7065646).OAIID:RECH_ACHV_DSTSH_NO:A201608543RECH_ACHV_FG:RR00200003ADJUST_YN:EMP_ID:A002712CITE_RATE:DEPT_NM:컴퓨터공학부EMAIL:[email protected]_YN:CONFIRM: