Canonical Correlation Analysis Network Research Articles

Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning

BackgroundClassifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes.ResultsIn this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data.ConclusionsThe experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.

Tensor Canonical Correlation Analysis Networks for Multi-View Remote Sensing Scene Recognition

Convolutional neural network (CNN) has been proven an effective way to extract high-level features from remote sensing (RS) images automatically. Many variants of the CNN model have been proposed, including principal component analysis network (PCANet), canonical correlation analysis network (CCANet), multiple scale CCANet (MS-CCANet) and multiview CCANet (MCCANet). The PCANet is specialized for single view feature abstraction, while in many real-world practices, the RS data are frequently observed from many more views. Although CCANet, MS-CCANet and MCCANet can be applied to two or more view data, they consider only the pair-wise correlation by calculating a series of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">two-order</i> covariance matrices. However, the high-order consistence, which can only be explored by collectively and simultaneously examining all views, remains undiscovered. In this paper, we propose the tensor canonical correlation analysis network (TCCANet) to tackle this problem. Particularly, TCCANet learns filter banks by simultaneously maximizing arbitrary number of views with high-order-correlation and solves the optimization problem by decomposing a covariance tensor. After the convolutional stage, we utilize binarization and block-wise histogram strategies to generate the final feature. Furthermore, we also develop a Multiple Scale version of TCCANet, i.e., MS-TCCANet, to extract enriched representation of the RS data by incorporating all previous convolutional layers. Numerical experiment results on RSSCN7 and SAT-6 datasets demonstrate the advantages of TCCANet and MS-TCCANet for RS scene recognition.

A PCA–CCA network for RGB-D object recognition

Object recognition is one of the essential issues in computer vision and robotics. Recently, deep learning methods have achieved excellent performance in red-green-blue (RGB) object recognition. However, the introduction of depth information presents a new challenge: How can we exploit this RGB-D data to characterize an object more adequately? In this article, we propose a principal component analysis–canonical correlation analysis network for RGB-D object recognition. In this new method, two stages of cascaded filter layers are constructed and followed by binary hashing and block histograms. In the first layer, the network separately learns principal component analysis filters for RGB and depth. Then, in the second layer, canonical correlation analysis filters are learned jointly using the two modalities. In this way, the different characteristics of the RGB and depth modalities are considered by our network as well as the characteristics of the correlation between the two modalities. Experimental results on the most widely used RGB-D object data set show that the proposed method achieves an accuracy which is comparable to state-of-the-art methods. Moreover, our method has a simpler structure and is efficient even without graphics processing unit acceleration.

Multiview Canonical Correlation Analysis Networks for Remote Sensing Image Recognition

In the past decade, deep learning (DL) algorithms have been widely used for remote sensing (RS) image recognition tasks. As the most typical DL model, convolutional neural networks (CNNs) achieves outstand performance for big RS data classification. Recently, a variant of CNN, dubbed canonical correlation analysis network (CCANet), was proposed to abstract the two-view image features. Extensive experiments conducted on several benchmark databases validate the effectiveness of CCANet. However, the CCANet structure is powerless when the observations arrive from more than two sources. To serve the multiview purpose, in this letter, we propose multiview CCANets (MCCANets). Particularly, the MCCANet model learns the stacked multiperspective filter banks by the MCCA method and builds a deep convolutional structure. In the output stage, the binarization and the blockwise histogram are employed as nonlinear processing and feature pooling, respectively. To access the effectiveness of the MCCANet, we conduct a host of experiments on the RSSCN7 RS database. Extensive experimental results demonstrate that the MCCANet outperforms the two-view CCANet.

Canonical correlation analysis networks for two-view image recognition

In recent years, deep learning has attracted an increasing amount of attention in machine learning and artificial intelligence areas. Currently, many deep learning network-related architectures such as deep neural networks (DNNs), convolutional neural network (CNN), wavelet scattering network (ScatNet) and principal component analysis network (PCANet) have been proposed. The most effective network is PCANet, which has achieved promising performance in image classification, such as for face, object and handwritten digit recognition. PCANet can only handle data that are represented by single-view features. In this paper, we present a canonical correlation analysis network (CCANet) to address image classification, in which images are represented by two-view features. The CCANet learns two-view multistage filter banks by a canonical correlation analysis (CCA) method and constructs a cascaded convolutional deep network. Then, we incorporate filters with binaryzation and block-wise histogram processes to form the final depth structure. In addition, we introduce a variation of CCANet—dubbed RandNet-2—in which the filter banks are randomly generated. Extensive experiments are conducted using the ETH-80, Yale-B, and USPS databases for object classification, face classification and handwritten digits classification, respectively. The experimental results demonstrate that the CCANet algorithm is more effective than PCANet, RandNet-1 and RandNet-2.