Sung-Hsien HsiehChun-Shien LuSOO-CHANG PEI2019-10-242019-10-24201615224880https://scholars.lib.ntu.edu.tw/handle/123456789/428085https://www.scopus.com/inward/record.uri?eid=2-s2.0-85006802378&doi=10.1109%2fICIP.2016.7532666&partnerID=40&md5=8548157d563565dd3928fdb8c50975fcBinary embedding of high-dimensional data aims to produce low-dimensional binary codes while preserving discriminative power. State-of-the-art methods often suffer from high computation and storage costs. We present a simple and fast embedding scheme by first downsampling N-dimensional data into M-dimensional data and then multiplying the data with an M×M circulant matrix. Our method requires O(N + M log M) computation and O(N) storage costs. We prove if data have sparsity, our scheme can achieve similarity-preserving well. Experiments further demonstrate that though our method is cost-effective and fast, it still achieves comparable performance in image applications. © 2016 IEEE.Circulant matrix; Dimensionality reduction; Embedding; Random projection; Subsampling[SDGs]SDG10Bins; Clustering algorithms; Cost effectiveness; Costs; Digital storage; Image processing; Circulant matrix; Dimensionality reduction; Embedding; Random projections; Subsampling; Matrix algebraconference paper10.1109/icip.2016.75326662-s2.0-85006802378