Structured sparse PCA to identify miRNA co-regulatory modules

Ren, Shaogang; Qian, Xiaoning

doi:10.1109/icassp.2014.6854116

Cited by 3 publications

(5 citation statements)

References 12 publications

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“…Through experiment, EMI signals enhanced by this method are classified more accurately, which may provide a new approach for EMI signal identification. [2] 51.61% LLE [31] 60.29% MDS [32] 58.92% ISOMAP [33] 57.84%…”

Section: Resultsmentioning

confidence: 99%

“…In addition, we also compare our method with the classic feature extraction methods, including PCA [2], local linear embedding (LLE) [31], multidimensional scaling (MDS) [32], and ISOMAP [33]. We also use support vector machine (SVM) as the classifier and the result is shown in Table 4.…”

Section: Experiments and Analysismentioning

confidence: 99%

“…Common methods for signal processing includes: independent component analysis [1], principle component analysis (PCA) [2], wavelet transform [3], Hilbert Huang transform [4], subspace learning [5][6][7], empirical mode decomposition [5,[7][8][9][10][11], and clustering [12][13][14][15]. To all of these methods, it is always crucial to determine how to select and extract features from a signal, especially from those high-dimension signals.…”

Section: Introductionmentioning

confidence: 99%

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EMI signal feature enhancement based on extreme energy difference and deep auto‐encoder

Chen

et al. 2018

IET signal process.

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To enhance features of different electromagnetic interference (EMI) signals, which are significant for further feature extraction and pattern recognition, the authors propose an EMI signal feature enhancement method based on extreme energy difference and a deep auto-encoder. Experimental results show that this method can effectively enhance features of EMI signals and improve recognition accuracy.

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Section: Resultsmentioning

confidence: 99%

Section: Experiments and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EMI signal feature enhancement based on extreme energy difference and deep auto‐encoder

Chen

et al. 2018

IET signal process.

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“…Nonetheless, the convex structured sparsity constraints in [10] may not be necessarily consist with real-world applications. In order to capture more flexible and general structure, Ren et al [11] introduced binary matrices as auxiliary variables and proposed Markov Random Field (MRF) based SSPCA (MS 2 PCA) for gene interaction. It is worth noting that the methods mentioned above worked off-line.…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, we aim to learn the interpretable structural elements (e.g., mouth, eyes, or forehead in face images), which generally leads to a low-rank image in contrast to the full-rank original image. In [10][11][12], using the predefined structural constraints in factors is away from the actual situation and cannot depict complex structural information. Here we use nuclear norm to capture the structures in data.…”

Section: Introductionmentioning

confidence: 99%

Schatten p-norm based principal component analysis

et al. 2016

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Structured sparse PCA (SSPCA) is a new emerging method regularized by structured sparsity-inducing norms. However, these regularization terms are not necessarily optimal because of the noisy and irrelevant features embedded in predefined patterns. This paper presents a method called Schatten p-norm based principal component analysis (S p PCA) to learn interpretable and structured elements (or factors). In S p PCA, a low-rank assumption is used to characterize structured elements in a two-dimensional matrix form. Compared to SSPCA, the low-rank assumption of S p PCA is more intuitive and effective for describing object parts of an image. Moreover, S p PCA can deal with some scenarios, where the dictionary element matrixes have complex structures. We also propose an efficient and simple optimization procedure to solve the problem. Extensive experiments on denoising of sparse structured signals and face recognition on different databases (e.g. AR, Extend Yale B and Multi-PIE) demonstrate the superior performance over some recently proposed methods.

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Safe Feature Screening for Generalized LASSO

Ren

Huang

et al. 2018

IEEE Trans. Pattern Anal. Mach. Intell.

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Solving Generalized LASSO (GL) problems is challenging, particularly when analyzing many features with a complex interacting structure. Recent developments have found effective ways to identify inactive features so that they can be removed or aggregated to reduce the problem size before applying optimization solvers for learning. However, existing methods are mostly devoted to special cases of GL problems with special structures for feature interactions, such as chains or trees. Developing screening rules, particularly, safe screening rules to remove or aggregate features with general interaction structures, calls for a very different screening approach for GL problems. To tackle this challenge, we formulate the GL screening problem as a bound estimation problem in a large linear inequality system when solving them in the dual space. We propose a novel bound propagation algorithm for efficient safe screening for general GL problems, which can be further enhanced by developing novel transformation methods that can effectively decouple interactions among features. The proposed propagation and transformation methods are applicable with dynamic screening that can easily initiate the screening process while existing screening methods require the knowledge of the solution under a desirable regularization parameter. Experiments on both synthetic and real-world data demonstrate the effectiveness of the proposed screening method.

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Structured sparse PCA to identify miRNA co-regulatory modules

Cited by 3 publications

References 12 publications

EMI signal feature enhancement based on extreme energy difference and deep auto‐encoder

EMI signal feature enhancement based on extreme energy difference and deep auto‐encoder

Schatten p-norm based principal component analysis

Safe Feature Screening for Generalized LASSO

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