Unsupervised feature selection chooses a subset of discriminative features to reduce feature
dimension under the unsupervised learning paradigm. Although lots of efforts have been …

Cross-modal hashing has attracted considerable attention for large-scale multimodal
retrieval task. A majority of hashing methods have been proposed for cross-modal retrieval …

Due to the rapid growth of multi-modal data, hashing methods for cross-modal retrieval have
received considerable attention. However, finding content similarities between different …

This paper focuses on analytics of an extremely large dataset of smart grid electricity price
and load, which is difficult to process with conventional computational models. These data …

The in-memory algorithms for approximate nearest neighbor search (ANNS) have achieved
great success for fast high-recall search, but are extremely expensive when handling very …

Finding nearest neighbors in high-dimensional spaces is a fundamental operation in many
diverse application domains. Locality Sensitive Hashing (LSH) is one of the most popular …

Hashing has shown great potential in large-scale image retrieval due to its storage and
computation efficiency, especially the recent deep supervised hashing methods. To achieve …

The deep hashing based retrieval method is widely adopted in large-scale image and video
retrieval. However, there is little investigation on its security. In this paper, we propose a …

The climate change crisis, exacerbated by the global dependency of fossil fuels, has brought
significant challenges. In the medium to long term, extensive renewable-energy-based …

This paper proposes a novel deep polarized network (DPN) for learning to hash, in which
each channel in the network outputs is pushed far away from zero by employing a …