Does generalization performance of lql^q regularization learning depend on qq? A negative example

$l^q$-regularization has been demonstrated to be an attractive technique in machine learning and statistical modeling. It attempts to improve the generalization (prediction) capability of a machine (model) through appropriately shrinking its coefficients. The shape of a $l^q$ estimator differs in varying choices of the regularization order $q$. In particular, $l^1$ leads to the LASSO estimate, while $l^{2}$ corresponds to the smooth ridge regression. This makes the order $q$ a potential tuning parameter in applications. To facilitate the use of $l^{q}$-regularization, we intend to seek for a modeling strategy where an elaborative selection on $q$ is avoidable. In this spirit, we place our investigation within a general framework of $l^{q}$-regularized kernel learning under a sample dependent hypothesis space (SDHS). For a designated class of kernel functions, we show that all $l^{q}$ estimators for $0< q < \infty$ attain similar generalization error bounds. These estimated bounds are almost optimal in the sense that up to a logarithmic factor, the upper and lower bounds are asymptotically identical. This finding tentatively reveals that, in some modeling contexts, the choice of $q$ might not have a strong impact in terms of the generalization capability. From this perspective, $q$ can be arbitrarily specified, or specified merely by other no generalization criteria like smoothness, computational complexity, sparsity, etc..Comment: 35 pages, 3 figure

Making with Shenzhen (Characteristics)—Strategy and Everyday Tactics in a City’s Creative Turn

This paper investigates the government-led maker movement in Shenzhen, China by deploying Michel de Certeau’s concepts of “strategy” and “tactics”. While there is a growing body of literature surrounding the maker movement, the discrepancy between the maker movement presented in urban policies and its participants’ actual practices is underexplored. Situating the exploration in the Chinese context, this article looks into how state intervention shapes the maker movement and actors’ participation. This work starts with considerations of political economy to demonstrate how the “Make with Shenzhen” campaign as a strategy fits into the government’s creative city agenda. It then draws upon the findings of a longitudinal ethnographic study to illuminate how discourses, institutions and apparatuses are tactically appropriated by individuals to mobilize symbolic, monetary, social and political resources to serve their interests. We argue that these tactical practices can potentially lead to meaningful changes in the city of Shenzhen and the everyday life of its people. By juxtaposing the strategy of the “Make with Shenzhen” campaign with the tactical practices surrounding it, this study offers insight into the challenges and possibilities brought about by the city-wide learning and making in the Chinese context