Self-taught dimensionality reduction on the high-dimensional small-sized data

Zhu, Xiaofeng; Huang, Zi; Yang, Yang; Shen, Heng Tao; Xu, Changsheng; Luo, Jiebo

doi:10.1016/j.patcog.2012.07.018

Cited by 131 publications

(51 citation statements)

References 34 publications

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“…The algorithm STDR [18] employs same regularizers as our JGSC but utilizes robust loss function 2 . Moreover, STDR learns the bases of training data from external data while JGSC obtains them from training data because STDR assumes learning with limited training data and JGSC does not make such assumption.…”

Section: Discussionmentioning

confidence: 99%

Feature Selection by Joint Graph Sparse Coding

Ding

Zhang

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

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This paper takes manifold learning and regression simultaneously into account to perform unsupervised spectral feature selection. We first extract the bases of the data, and then represent the data sparsely using the extracted bases by proposing a novel joint graph sparse coding model, JGSC for short. We design a new algorithm TOSC to compute the resulting objective function of JGSC, and then theoretically prove that the proposed objective function converges to its global optimum via the proposed TOSC algorithm. We repeat the extraction and the TOSC calculation until the value of the objective function of JGSC satisfies pre-defined conditions. Eventually the derived new representation of the data may only have a few non-zero rows, and we delete the zero rows (a.k.a. zero-valued features) to conduct feature selection on the new representation of the data. Our empirical studies demonstrate that the proposed method outperforms several state-of-the-art algorithms on real datasets in term of the kNN classification performance.

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Section: Discussionmentioning

confidence: 99%

Feature Selection by Joint Graph Sparse Coding

Ding

Zhang

et al. 2013

Proceedings of the 2013 SIAM International Conference on Data Mining

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“…By observing Eq.5, we find that both E i and D depend on the value ofW. In this paper, following the literatures [18,42], we design a novel iterative algorithm (i.e., Algorithm 1) to optimize Eq.4 and then prove its convergence. Here we introduce Theorem 1 to guarantee that Eq.4 monotonically decreases in each iteration of Algorithm 1.…”

Section: Optimizationmentioning

confidence: 95%

“…For example, the local feature HSV is less robust to the changes in frame rate, video length, captions. SIFT is sensitive to changes in contrast, brightness, scale, rotation, camera viewpoint, and so on [13,42].…”

Section: Introductionmentioning

confidence: 99%

Multi-view multi-sparsity kernel reconstruction for multi-class image classification

et al. 2015

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This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.

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“…As in most dictionary learning methods [45], [50], the LVE step in the proposed hashing algorithm is not jointly convex to Φ and Λ, but it is convex to each of them when another one is fixed. In the alternative optimization, the energy will decrease step by step.…”

Section: Algorithm 1: the Algorithm Of E-selve: Training Stagementioning

confidence: 99%

A Sparse Embedding and Least Variance Encoding Approach to Hashing

Zhu

Zhang

Huang

2014

IEEE Trans. on Image Process.

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217

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Abstract-Hashing is becoming increasingly important in large-scale image retrieval for fast approximate similarity search and efficient data storage. Many popular hashing methods aim to preserve the kNN graph of high dimensional data points in the low dimensional manifold space, which is however difficult to achieve when the number of samples is big. In this paper, we propose an effective and efficient hashing approach by sparsely embedding a sample in the training sample space and encoding the sparse embedding vector over a learned dictionary. To this end, we partition the sample space into clusters via a linear spectral clustering method, and then represent each sample as a sparse vector of normalized probabilities that it falls into its several closest clusters. This actually embeds each sample sparsely in the sample space. The sparse embedding vector is employed as the feature of each sample for hashing. We then propose a least variance encoding model, which learns a dictionary to encode the sparse embedding feature, and consequently binarize the coding coefficients as the hash codes. The dictionary and the binarization threshold are jointly optimized in our model. Experimental results on benchmark datasets demonstrated the effectiveness of the proposed approach in comparison with stateof-the-art methods.Index Terms-hashing, manifold learning, image retrieval, dictionary learning.

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Self-taught dimensionality reduction on the high-dimensional small-sized data

Cited by 131 publications

References 34 publications

Feature Selection by Joint Graph Sparse Coding

Feature Selection by Joint Graph Sparse Coding

Multi-view multi-sparsity kernel reconstruction for multi-class image classification

A Sparse Embedding and Least Variance Encoding Approach to Hashing

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