English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Dantzig Selector (DS) is widely used in compressed sensing and sparselearning for feature selection and sparse signal recovery. Since the DSformulation is essentially a linear programming optimization, many existinglinear programming solvers can be simply applied for scaling up. The DSformulation can be explained as a basis pursuit denoising problem, wherein thedata matrix (or measurement matrix) is employed as the denoising matrix toeliminate the observation noise. However, we notice that the data matrix maynot be the optimal denoising matrix, as shown by a simple counter-example. Thismotivates us to pursue a better denoising matrix for defining a general DSformulation. We first define the optimal denoising matrix through a minimaxoptimization, which turns out to be an NPhard problem. To make the problemcomputationally tractable, we propose a novel algorithm, termed as OptimalDenoising Dantzig Selector (ODDS), to approximately estimate the optimaldenoising matrix. Empirical experiments validate the proposed method. Finally,a novel sparse reinforcement learning algorithm is formulated by extending theproposed ODDS algorithm to temporal difference learning, and empiricalexperimental results demonstrate to outperform the conventional vanilla DS-TDalgorithm.

Dantzig Selector with an Approximately Optimal Denoising Matrix and its Application to Reinforcement Learning