In this paper, we proposed a more robust supervised hashing framework based on the Cauchy loss function and Supervised Discrete Hashing (SDH) called Robust (RSDH), which can learn subspace consisted of binary codes. The is used to measure error between label matrix product decomposed matrices. RSDH not only reduce outliers noise codes, but also achieve satisfactory retrieval effect. Image exper...

The hashing methods have been widely used for efficient similarity retrieval on large scale image datasets. The traditional hashing methods learn hash functions to generate binary codes from hand-crafted features, which achieve limited accuracy since the hand-crafted features cannot optimally represent the image content and preserve the semantic similarity. Recently, several deep hashing method...

Linear Support Vector Machines (e.g., SVM, Pegasos, LIBLINEAR) are powerful and extremely efficient classification tools when the datasets are very large and/or highdimensional, which is common in (e.g.,) text classification. Minwise hashing is a popular technique in the context of search for computing resemblance similarity between ultra high-dimensional (e.g., 2) data vectors such as document...

Hashing is a basic tool for dimensionality reduction employed in several aspects of machine learning. However, the perfomance analysis is often carried out under the abstract assumption that a truly random unit cost hash function is used, without concern for which concrete hash function is employed. The concrete hash function may work fine on sufficiently random input. The question is if they c...

Dynamic hashing, while surpassing other access methods for uniformly distributed data, usually performs badly for non-uniformly distributed data. We propose a robust scheme for multi-level extendible hashing allowing efficient processing of skewed data as well as uniformly distributed data. In order to test our access method we implemented it and compared it to several existing hashing schemes....