Safe Screening With Variational Inequalities and Its Application to LASSO

Sparse learning techniques have been routinely used for feature selection as the resulting model usually has a small number of non-zero entries. Safe screening, which eliminates the features that are guaranteed to have zero coefficients for a certain value of the regularization parameter, is a technique for improving the computational efficiency. Safe screening is gaining increasing attention since 1) solving sparse learning formulations usually has a high computational cost especially when the number of features is large and 2) one needs to try several regularization parameters to select a suitable model. In this paper, we propose an approach called "Sasvi" (Safe screening with variational inequalities). Sasvi makes use of the variational inequality that provides the sufficient and necessary optimality condition for the dual problem. Several existing approaches for Lasso screening can be casted as relaxed versions of the proposed Sasvi, thus Sasvi provides a stronger safe screening rule. We further study the monotone properties of Sasvi for Lasso, based on which a sure removal regularization parameter can be identified for each feature. Experimental results on both synthetic and real data sets are reported to demonstrate the effectiveness of the proposed Sasvi for Lasso screening.Comment: Accepted by International Conference on Machine Learning 201

Identification of male- and female-specific olfaction genes in antennae of the oriental fruit fly (Bactrocera dorsalis)

The oriental fruit fly (Bactrocera dorsalis) is a species of tephritid fruit fly, endemic to Southeast Asia but also introduced to many regions of the US, and it is one of the major pest species with a broad host range of cultivated and wild fruits. Although males of B. dorsalis respond strongly to methyl eugenol and this is used for monitoring and estimating populations, the molecular mechanism of the oriental fruit fly olfaction has not been elucidated yet. Therefore, in this project, using next generation sequencing technologies, we sequenced the transcriptome of the antennae of male and female adults of B. dorsalis. We identified a total of 20 candidate odorant binding proteins (OBPs), 5 candidate chemosensory proteins (CSPs), 35 candidate odorant receptors (ORs), 12 candidate ionotropic receptors (IRs) and 4 candidate sensory neuron membrane proteins (SNMPs). The sex-specific expression of these genes was determined and a subset of 9 OR genes was further characterized by qPCR with male and female antenna, head, thorax, abdomen, leg and wing samples. In the male antennae, 595 genes showed a higher expression, while 128 genes demonstrated a higher expression in the female antennae. Interestingly, 2 ORs (BdorOR13 and BdorOR14) were highly and specifically expressed in the antennae of males, and 4 ORs (BdorOR13, BdorOR16, BdorOR18 and BdorOR35) clustered with DmOR677, suggesting pheromone reception. We believe this study with these antennae-enriched OBPs, CSPs, ORs, IRs and SNMPs can play an important role in the detection of pheromones and general odorants, and so in turn our data improve our current understanding of insect olfaction at the molecular level and provide important information for disrupting the behavior of the oriental fruit fly using chemical communication methods