Haichuan Yang

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Band selection is an important step towards effective and efficient object classification in hyperspectral imagery. In this paper, we propose a semi-supervised learning method for band selection based on a sparse linear regression model. This model uses a least absolute shrinkage and selection operator to compute the regression coefficients from both(More)
The p-stable distribution is traditionally used for data-independent hashing. In this paper, we describe how to perform data-dependent hashing based on p-stable distribution. We commence by formulating the Euclidean distance preserving property in terms of variance estimation. Based on this property, we develop a projection method, which maps the original(More)
Hashing is very useful for fast approximate similarity search on large database. In the unsupervised settings, most hashing methods aim at preserving the similarity defined by Euclidean distance. Hash codes generated by these approaches only keep their Hamming distance corresponding to the pairwise Euclidean distance, ignoring the local distribution of each(More)
This paper considers the feature selection scenario where only a few features are accessible at any time point. For example, features are generated sequentially and visible one by one. Therefore, one has to make an online decision to identify key features after all features are only scanned once or twice. The optimization based approach is a powerful tool(More)
On a typical university campus, the words "massive data storage" (MDS) usually bring to mind technology high-end, high performance computing (HPC) users might use. This is because academic supercomputer sites have traditionally provided a tightly interwoven HPC and high performance, MDS fabric to their users for decades. However, a new paradigm in data(More)
Hashing is a useful tool for contents-based image retrieval on large scale database. This paper presents an unsupervised data-dependent hashing method which learns similarity preserving binary codes. It uses p-stable distribution and coordinate descent method to achieve a good approximate solution for an acknowledged objective of hashing. This method(More)
Sparse feature (dictionary) selection is critical for various tasks in computer vision, machine learning, and pattern recognition to avoid overfitting. While extensive research efforts have been conducted on feature selection using sparsity and group sparsity, we note that there has been a lack of development on applications where there is a particular(More)
Binary code is a kind of special representation of data. With the binary format, hashing framework can be built and a large amount of data can be indexed to achieve fast research and retrieval. Many supervised hashing approaches learn hash functions from data with supervised information to retrieve semantically similar samples. This kind of supervised(More)
Imaging devices are of increasing use in environmental research requiring an urgent need to deal with such issues as image data, feature matching over different dimensions. Among them, matching hyperspectral image with other types of images is challenging due to the high dimensional nature of hyperspectral data. This chapter addresses this problem by(More)