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Machine learning with automatic feature selection for multi-class protein fold classification
(2005-07-01)
Huang, CD
;
Liang, SF
;
Lin, CT
;
Wu, RC
;
電控工程研究所
In machine learning, both the properly used networks and the selected features are important factors which should be considered carefully. These two factors will influence the result, whether for better or worse. In bioinformatics, the amount of features may be very large to make machine learning possible. In this study we introduce the idea of feature selection in the problem of bioinformatics. We use neural networks to complete our task where each input node is associated with a gate. At the beginning of the training, all gates are almost closed, and, at this time, no features are allowed to enter the network. During the training phase, gates are either opened or closed, depending oil the requirements. After the selection training phase has completed, gates corresponding to the helpful features are completely opened while gates Corresponding to the useless features are closed more tightly. Some gates may be partially open, depending oil the importance of the corresponding features. So, the network can not only select features in an online manner during learning, but it also does some feature extraction. We combine feature selection with our novel hierarchical machine learning architecture and apply it to multi-class protein fold classification. At the first level the network classifies the data into four major folds: all alpha, all beta, alpha + beta and alpha/beta. In the next level, we have another set of networks which further classifies the data into twenty-seven folds. This approach helps achieve the following. The gating network is found to reduce the number of features drastically. It is interesting to observe that, for the first level using just 50 features selected by the gating network, we can get a test accuracy comparable to that using 125 features in neural classifiers. The process also helps us get a better insight into the folding process. For example, tracking the evolution of different gates, we call find which characteristics (features) of the data are more important for the folding process. Eventually, it reduces the computation time. The use of the hierarchical architecture helps LIS get a better performance also.
Neural-network-based adaptive hybrid-reflectance model for 3-D surface reconstruction
(2005-11-01)
Lin, CT
;
Cheng, WC
;
Liang, SF
;
生物科技學系
;
資訊工程學系
This paper proposes a novel neural-network-based adaptive hybrid-reflectance three-dimensional (3-D) surface reconstruction model. The neural network automatically combines the diffuse and specular components into a hybrid model. The proposed model considers the characteristics of each point and the variant albedo to prevent the reconstructed surface from being distorted. The neural network inputs are the pixel values of the two-dimensional images to be reconstructed. The normal vectors of the surface can then be obtained from the output of the neural network after supervised learning, where the illuminant direction does not have to be known in advance. Finally, the obtained normal vectors are applied to enforce integrability when reconstructing 3-D objects. Facial images and images of other general objects were used to test the proposed approach. The experimental results demonstrate that the proposed neural-network-based adaptive hybrid-reflectance model can be successfully applied to objects generally and perform 3-D surface reconstruction better than some existing approaches.
A 3-D surface reconstruction approach based on postnonlinear ICA model
(2005-11-01)
Lin, CT
;
Cheng, WC
;
Liang, SF
;
生物科技學系
;
資訊工程學系
Photometric stereo technique deals with the reconstruction of three-dimensional (3-D) shape of an object by using several images of the same surface taken from the same viewpoint but under illuminations from different directions. In this paper, we propose a new photometric stereo scheme based on a new reflectance model and the postnonlinear (PNL) independent components analysis (ICA) method. The proposed nonlinear reflectance model consists of diffuse components and specular components for modeling the surface reflectance of a stereo object in an image. Unlike the previous approaches, these two components are not separated and processed individually in the proposed model. An unsupervised learning adaptation algorithm is developed to estimate the reflectance model based on image intensities. In this algorithm, the PNL ICA method is used to obtain the surface normal on each point of an image. Then, the 3-D surface model is reconstructed based on the estimated surface normal on each point of image by using the enforcing integrability method. Two experiments are performed to assess the performance of the proposed approach. We test our algorithm on synthetically generated images for the reconstruction of surface of objects and on a number of real images captured from the Yale Face Database B. These testing images contain variability due to illumination and varying albedo in each point of surface of human faces. All the experimental results are compared to those of the existing photometric stereo approaches tested on the same images. The results clearly indicate the superiority of the proposed nonlinear reflectance model over the conventional Lambertian model and the other linear hybrid reflectance model.

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