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As graph technology is widely used in unsupervised dimensionality reduction, many methods automatically construct a full connection graph to learn the structure of data, and then preserve critical information on data in subspace. The construction of a full connection graph with heavy computational complexity, however, is separated from the optimization of transformation matrix. In order to solve significant computational burden, we design anchor-based graph and unify the construction of graph and optimization of transformation matrix into a framework called fast unsupervised embedding learning with anchor-based graph (FUAG) which not only can avoid the impact of noises and redundant features in original space, but also can capture local structure of data in subspace precisely. Our method additionally incorporates the discriminant information of data captured by using trace difference form. Meanwhile, it optimizes the anchor-based graph partitioning problem with Constrained Laplacian Rank in order to ensure that the number of connected components is exactly equal to the number of classes. We also impose ℓ 0 norm constraint on each point to avoid trivial solutions and propose an efficient iterative algorithm. Experimental results on both synthetic and real-world datasets demonstrate the promising performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Effective dimensionality reduction is a classical research area for many large-scale analysis tasks in computer vision. Several recent methods attempt to learn either graph embedding or binary hashing for fast and accurate applications. In this paper, we propose a novel framework to automatically learn the task-specific compact coding, called evolutionary compact embedding (ECE), which can be regarded as an optimization algorithm combining genetic programming (GP) and a boosting trick. As an evolutionary computation methodology, GP can solve problems inspired by natural evolution without any prior knowledge of the solutions. In our evolutionary architecture, each bit of ECE is iteratively computed using a binary classification function, which is generated through GP evolving by jointly minimizing its empirical risk with the AdaBoost strategy on a training set. We address this as greedy optimization leading to small Hamming distances for similar samples and large distances for dissimilar samples. We then evaluate ECE on four image datasets: USPS digital hand-writing, CMU PIE face, CIFAR-10 tiny image and SUN397 scene, showing the accurate and robust performance of our method for large-scale image classification. [ABSTRACT FROM AUTHOR]