On sparse linear discriminant analysis algorithm for high‐dimensional data classification

Ng, Michael K.; Liao, Li-Zhi; Zhang, Leihong

doi:10.1002/nla.736

Cited by 15 publications

(7 citation statements)

References 27 publications

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“…Therefore, speed variables were excluded in this step, while distance variables were kept for further analysis, which resulted in 31 behavioural measures included in the LDA. Finally, we applied the Moore-Pseudo Inverse method to allow inclusion of all variables in the analysis by approximating the inverse of the within variance matrix (100). This last step was necessary because one of LDA’s criteria is that the total number of variables analysed must be lower or equal to the total number of samples minus the number of classes (98).…”

Section: Methodsmentioning

confidence: 99%

Activated PI3Kδ syndrome, an immunodeficiency disorder, leads to sensorimotor deficits recapitulated in a murine model

Serra

Manusama

Kaiser

et al. 2021

Preprint

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The phosphoinositide-3-kinase (PI3K) family plays a major role in cell signalling and is predominant in leukocytes. Gain-of-function (GOF) mutations in the PIK3CD gene lead to the development of activated PI3Kδ syndrome (APDS), a rare primary immunodeficiency disorder. A subset of APDS patients also displays neurodevelopmental delay symptoms, suggesting a potential role of PIK3CD in cognitive and behavioural function. However, the extent and nature of the neurodevelopmental deficits has not been previously quantified. Here, we assessed the cognitive functions of two APDS patients, and investigated the causal role of the PIK3CD GOF mutation in neurological deficits using a murine model of this disease. We used E1020K knock-in mice, harbouring the most common APDS mutation in patients. We found that APDS patients present with visuomotor deficits, exacerbated by autism spectrum disorder comorbidity, whereas p110δE1020K mice exhibited impairments in motor behaviour, learning and repetitive behaviour patterning. Our data indicate that PIK3CD GOF mutations increase the risk for neurodevelopmental deficits, supporting previous findings on the interplay between the nervous and the immune system. Further, our results validate the knock-in mouse model, and offer an objective assessment tool for patients that could be incorporated in diagnosis and in the evaluation of treatments.

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Section: Methodsmentioning

confidence: 99%

Activated PI3Kδ syndrome, an immunodeficiency disorder, leads to sensorimotor deficits recapitulated in a murine model

Serra

Manusama

Kaiser

et al. 2021

Preprint

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“…LDA is a normally used method for classification [37][38][39]. Suppose a set of N samples {x 1 , x 2 , .…”

Section: Ldamentioning

confidence: 99%

Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

Liu

et al. 2019

Remote Sensing

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Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

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“…To improve the ability of the model to interpret and visualize the results, A 1 (= i, j |A i j |) is incorporated into the new feature representation. Sparse representation of features for some real data sets has been studied and reported (see Dundar, Fung, Bi, Sathyakama, & Rao, 2005;Fung & Ng, 2007;Qiao, Zhou, & Huang, 2009;Ng, Liao, & Zhang, 2011) and references therein.…”

Section: 1mentioning

confidence: 99%

Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality

Zhu

Liao

2018

Neural Computation

Self Cite

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We study a multi-instance (MI) learning dimensionality-reduction algorithm through sparsity and orthogonality, which is especially useful for high-dimensional MI data sets. We develop a novel algorithm to handle both sparsity and orthogonality constraints that existing methods do not handle well simultaneously. Our main idea is to formulate an optimization problem where the sparse term appears in the objective function and the orthogonality term is formed as a constraint. The resulting optimization problem can be solved by using approximate augmented Lagrangian iterations as the outer loop and inertial proximal alternating linearized minimization (iPALM) iterations as the inner loop. The main advantage of this method is that both sparsity and orthogonality can be satisfied in the proposed algorithm. We show the global convergence of the proposed iterative algorithm. We also demonstrate that the proposed algorithm can achieve high sparsity and orthogonality requirements, which are very important for dimensionality reduction. Experimental results on both synthetic and real data sets show that the proposed algorithm can obtain learning performance comparable to that of other tested MI learning algorithms.

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On sparse linear discriminant analysis algorithm for high‐dimensional data classification

Cited by 15 publications

References 27 publications

Activated PI3Kδ syndrome, an immunodeficiency disorder, leads to sensorimotor deficits recapitulated in a murine model

Activated PI3Kδ syndrome, an immunodeficiency disorder, leads to sensorimotor deficits recapitulated in a murine model

Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

Multi-Instance Dimensionality Reduction via Sparsity and Orthogonality

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