Review of the book Authors: Federica Doglio, Mirko Zardini Edited by: Federica Doglio Title: Dopo le Crisi Subtitle: 1978, 2001, 2008, 2020 Language: Italiano Publisher: LetteraVentidue ...Characteristic: Format 10x15cm forma, 128 pages, paperback, monocrome ISBN: 978-88-6242-419-6 Year: 2021
Brain data recorded with electroencephalography (EEG), magnetoencephalography (MEG) and related techniques often have poor signal-to-noise ratios due to the presence of multiple competing sources and ...artifacts. A common remedy is to average responses over repeats of the same stimulus, but this is not applicable for temporally extended stimuli that are presented only once (speech, music, movies, natural sound). An alternative is to average responses over multiple subjects that were presented with identical stimuli, but differences in geometry of brain sources and sensors reduce the effectiveness of this solution. Multiway canonical correlation analysis (MCCA) brings a solution to this problem by allowing data from multiple subjects to be fused in such a way as to extract components common to all. This paper reviews the method, offers application examples that illustrate its effectiveness, and outlines the caveats and risks entailed by the method.
•MCCA combines multiple data sets into a common representation.•MCCA can be used to summarize data across subjects.•MCCA can be used to denoise data, or reduce dimensionality, based on consistency across subjects.
Human age, gender and ethnicity are valuable demographic characteristics. They are also important soft biometric traits useful for human identification or verification. We present a framework that ...can estimate the three traits jointly. The joint estimation framework could deal with the mutual influence of age, gender, and ethnicity implicitly. Under this joint estimation framework, we explore different methods for simultaneous estimation of age, gender, and ethnicity. The canonical correlation analysis (CCA) based methods, and partial least squares (PLS) models are explored under our joint estimation framework. Both the linear and nonlinear methods are investigated to measure the performance. We also validate some extensions of these methods, such as the least squares formulations of the CCA methods. We found some consistent ranking of these methods under our joint estimation framework. More importantly, we found that the CCA based methods can derive an extremely low dimensionality in estimating age, gender and ethnicity. An analysis of this property is given based on the rank theory. The experiments are conducted on a very large database containing more than 55,000 face images.
•A framework for joint estimation of age, gender and ethnicity in a single step;•A novel finding on feature dimensionality in estimating age, gender and ethnicity;•A rank theory based analysis of dimensionality problem in using CCA based methods;•A ranking of CCA and PLS based methods under our joint estimation framework;•Investigation of LS formulations of the CCA based methods for our problem.
•Confirmatory composite analysis (CCA) can confirm measurement models using PLS-SEM.•CCA has benefits relative to confirmatory factor analysis (CFA).•CCA can confirm both reflective and formative ...measurement models.•Guidelines for the proper application of CCA are provided.•PLSpredict procedure for out-of-sample prediction with CCA is explained.•CCA also does not require fit to confirm measurement models.
Confirmatory factor analysis (CFA) has historically been used to develop and improve reflectively measured constructs based on the domain sampling model. Compared to CFA, confirmatory composite analysis (CCA) is a recently proposed alternative approach applied to confirm measurement models when using partial least squares structural equation modeling (PLS-SEM). CCA is a series of steps executed with PLS-SEM to confirm both reflective and formative measurement models of established measures that are being updated or adapted to a different context. CCA is also useful for developing new measures. Finally, CCA offers several advantages over other approaches for confirming measurement models consisting of linear composites.
Two-dimensional canonical correlation analysis (2D-CCA) is an effective and efficient method for two-view feature extraction and fusion. Since it is a global linear method, it fails to find the ...nonlinear correlation between different features. In contrast, in this paper we propose a novel two-view method named as two-dimensional locality preserving canonical correlation analysis (2D-LPCCA), which uses the neighborhood information to discover the intrinsic structure of data. In other words, it uses many local linear problems to approximate the global nonlinear case. In addition, inspired by sparsity preserving projections (SPP), the two-dimensional sparsity preserving canonical correlation analysis (2D-SPCCA) framework is also developed, which consists of three models. Experimental results on real world databases demonstrate the viability of the formulation, they also show that the classification results of our methods are higher than the other’s.
Cholangiocarcinoma accounts for approximately 10% of all hepatobiliary tumors and represents 3% of all new-diagnosed malignancies worldwide. Intrahepatic cholangiocarcinoma (i-CCA) accounts for 10% ...of all cases, perihilar (h-CCA) cholangiocarcinoma represents two-thirds of the cases, while distal cholangiocarcinoma accounts for the remaining quarter. Originally described by Klatskin in 1965, h-CCA represents one of the most challenging tumors for hepatobiliary surgeons, mainly because of the anatomical vascular relationships of the biliary confluence at the hepatic hilum. Surgery is the only curative option, with the goal of a radical, margin-negative (R0) tumor resection. Continuous efforts have been made by hepatobiliary surgeons in order to achieve R0 resections, leading to the progressive development of aggressive approaches that include extended hepatectomies, associating liver partition, and portal vein ligation for staged hepatectomy, pre-operative portal vein embolization, and vascular resections. i-CCA is an aggressive biliary cancer that arises from the biliary epithelium proximal to the second-degree bile ducts. The incidence of i-CCA is dramatically increasing worldwide, and surgical resection is the only potentially curative therapy. An aggressive surgical approach, including extended liver resection and vascular reconstruction, and a greater application of systemic therapy and locoregional treatments could lead to an increase in the resection rate and the overall survival in selected i-CCA patients. Improvements achieved over the last two decades and the encouraging results recently reported have led to liver transplantation now being considered an appropriate indication for CCA patients.
In this paper a face super-resolution method using two-dimensional canonical correlation analysis (2D CCA) is presented. A detail compensation step is followed to add high-frequency components to the ...reconstructed high-resolution face. Unlike most of the previous researches on face super-resolution algorithms that first transform the images into vectors, in our approach the relationship between the high-resolution and the low-resolution face image are maintained in their original 2D representation. In addition, rather than approximating the entire face, different parts of a face image are super-resolved separately to better preserve the local structure. The proposed method is compared with various state-of-the-art super-resolution algorithms using multiple evaluation criteria including face recognition performance. Results on publicly available datasets show that the proposed method super-resolves high quality face images which are very close to the ground-truth and performance gain is not dataset dependent. The method is very efficient in both the training and testing phases compared to the other approaches.
•A new face super-resolution (SR) method using 2D CCA is presented.•The method works directly on the 2D image without reshaping the image into vector.•A detail compensation step further enhances the super-resolved face images.•Experimental results show that our method outperforms current SR methods.•The proposed method is computationally efficient due to small matrices involved.
•Like latent variables, emergent variables serve to model abstract concepts.•Emergent variables can be assessed using confirmatory composite analysis (CCA).•CCA works in analogy to confirmatory ...factor analysis (CFA).•CCA deals with composite models, not reflective or formative measurement models.•The presentation of CCA by Hair et al. (2020) is misguided.
Confirmatory composite analysis (CCA) was invented by Jörg Henseler and Theo K. Dijkstra in 2014 and elaborated by Schuberth et al. (2018b) as an innovative set of procedures for specifying and assessing composite models. Composite models consist of two or more interrelated constructs, all of which emerge as linear combinations of extant variables, hence the term ‘emergent variables’. In a recent JBR paper, Hair et al. (2020) mistook CCA for the measurement model evaluation step of partial least squares structural equation modeling. In order to clear up potential confusion among JBR readers, the paper at hand explains CCA as it was originally developed, including its key steps: model specification, identification, estimation, and assessment. Moreover, it illustrates the use of CCA by means of an empirical study on business value of information technology. A final discussion aims to help analysts in business research to decide which type of covariance structure analysis to use.
Canonical analysis, a generalization of multiple regression to multiple‐response variables, is widely used in ecology. Because these models often involve many parameters (one slope per response per ...predictor), they pose challenges to model interpretation. Among these challenges, we lack quantitative frameworks for estimating the overall importance of single predictors in multi‐response regression models.
Here we demonstrate that commonality analysis and hierarchical partitioning, widely used for both estimating predictor importance and improving the interpretation of single‐response regression models, are related and complementary frameworks that can be expanded for the analysis of multiple‐response models.
In this application, we (a) demonstrate the mathematical links between commonality analysis, variation and hierarchical partitioning; (b) generalize these frameworks to allow the analysis of any number of predictor variables or groups of predictor variables as in the case of variation partitioning; and (c) introduce and demonstrate the implementation of these generalized frameworks in the R package rdacca.hp.
摘要
典范分析(RDA、dbRDA和CCA)作为多元回归应用于多响应变量的拓展，广泛应用于生态学数据分析。但由于典范分析通常涉及很多参数(即每个响应变量与每个解释变量之间都有一个系数)，因此在模型解读方面面临很多困难。其中有个尚未解决的挑战是缺乏定量的框架来评估解释变量相对重要性。
本研究中，我们证明了广泛用于估计多元回归模型解释变量重要性和提高模型解读性的共性分析(commonality analysis)和层次分割(hierarchical partitioning)是相关且互补的框架。我们也把层次分割框架扩展用于多响应变量的典范分析模型。
这里我们 a)展示了共性分析、变差分解(variation partitioning)和层次分割之间的数学联系；b)开发了不限制解释变量(组)数的变差分解和层次分割的R包rdacca.hp；c)使用Doubs鱼类数据演示rdacca.hp的使用和结果的解读。
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