Locality Preserving Projections for Grassmann manifold

Learning on Grassmann manifold has become popular in many computer vision tasks, with the strong capability to extract discriminative information for imagesets and videos. However, such learning algorithms particularly on high-dimensional Grassmann manifold always involve with significantly high computational cost, which seriously limits the applicability of learning on Grassmann manifold in more wide areas. In this research, we propose an unsupervised dimensionality reduction algorithm on Grassmann manifold based on the Locality Preserving Projections (LPP) criterion. LPP is a commonly used dimensionality reduction algorithm for vector-valued data, aiming to preserve local structure of data in the dimension-reduced space. The strategy is to construct a mapping from higher dimensional Grassmann manifold into the one in a relative low-dimensional with more discriminative capability. The proposed method can be optimized as a basic eigenvalue problem. The performance of our proposed method is assessed on several classification and clustering tasks and the experimental results show its clear advantages over other Grassmann based algorithms.Comment: Accepted by IJCAI 201

Wasserstein Introspective Neural Networks

We present Wasserstein introspective neural networks (WINN) that are both a generator and a discriminator within a single model. WINN provides a significant improvement over the recent introspective neural networks (INN) method by enhancing INN's generative modeling capability. WINN has three interesting properties: (1) A mathematical connection between the formulation of the INN algorithm and that of Wasserstein generative adversarial networks (WGAN) is made. (2) The explicit adoption of the Wasserstein distance into INN results in a large enhancement to INN, achieving compelling results even with a single classifier --- e.g., providing nearly a 20 times reduction in model size over INN for unsupervised generative modeling. (3) When applied to supervised classification, WINN also gives rise to improved robustness against adversarial examples in terms of the error reduction. In the experiments, we report encouraging results on unsupervised learning problems including texture, face, and object modeling, as well as a supervised classification task against adversarial attacks.Comment: Accepted to CVPR 2018 (Oral

Enhanced the prediction approach of diabetes using an autoencoder with regularization and deep neural network

Diabetes mellitus is considered one of the foremost common and extreme diseases worldwide. A precise and early diagnosis of diabetes is essential to avoid complications and is of crucial importance to the medical care that patients get. To achieve that, we need to develop a model to predict diabetes. There are many prediction models, but they suffer from some problems such as the accuracy of prediction being poor and the time complexity. The prediction process is highly dependent on important features. So, in this paper, we proposed a new model called (CAER-DNN) that depends on an unsupervised technique for generating newly important features and a deep neural network for the prediction process. The unsupervised technique is called complete autoencoder with regularization techniques (CAER) that uses to reconstruct the original features (newly learned features). It is focused too much on training the most important learned features and misses out on less important features. Thus, improving the performance of the prediction process. These important features are used as input to the deep neural network for the prediction of diabetes. Our model is applied to two sets of data including Pima Indian and Mendeley diabetic datasets. Based on the 10-fold cross-validation technique Pima Indian dataset achieves high performance in evaluation measures (f1-score 97.38%, accuracy, recall 97.25%, specificity 97.59%, precision 97.53%,). While the Mendeley diabetes dataset achieved high performance in evaluation measures (f1-score 94.51%, accuracy 98.48, recall 91.74%, accuracy-balance 98.21%, precision 98.21%) based on the holdout technique. compared with other existing machine learning and deep learning techniques our model outperformed existing techniques

Data-driven diagnosis of PEM fuel cell: A comparative study

International audienceThis paper is dedicated to data-driven diagnosis for Polymer Electrolyte Membrane Fuel Cell (PEMFC). More precisely, it deals with water related faults (flooding and membrane drying) by using pattern classification methodologies. Firstly, a method based on physical considerations is defined to label the training data. Secondly, a feature extraction procedure is carried out to pick up the significant features from vectors constructed by individual cell voltages. Finally, a classification is adopted in the feature space to realize the fault diagnosis. Various feature extraction and classification methodologies are employed on a 20-cell PEMFC stack. The performances of these methodologies are compared