When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery classification. However, these methods have limitations in generalizing well in challenging cases due to the limited representative capacity of the shallow feature learning model, as well as the insufficient robustness of the classifier which only depends on the supervision of labelled samples. To address these two problems simultaneously, we present an effective low-rank representation-based classification framework for hyperspectral imagery. In particular, a novel unsupervised segmented stacked denoising auto-encoder-based feature learning model is proposed to depict the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure. With the extracted features, a low-rank representation based robust classifier is then developed which takes advantage of both the supervision provided by labelled samples and unsupervised correlation (e.g., intra-class similarity and inter-class dissimilarity, etc.) among those unlabelled samples. Both the deep unsupervised feature learning and the robust classifier benefit, improving the classification accuracy with limited labelled samples. Extensive experiments on hyperspectral imagery classification demonstrate the effectiveness of the proposed framework.

/pdf/when-low-rank-representation-based-hyperspectral-imagery-4ccug3e3l8.pdf

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

In the research work of the brain-computer interface and the function of human brain work, the state classification of multitask state fMRI data is a problem. The fMRI signal of the human brain is a nonstationary signal with many noise effects and interference. Based on the commonly used nonstationary signal analysis method, Hilbert-Huang transform (HHT), we propose an improved circle-EMD algorithm to suppress the end effect. The algorithm can extract different intrinsic mode functions (IMFs), decompose the fMRI data to filter out low frequency and other redundant noise signals, and more accurately reflect the true characteristics of the original signal. For the filtered fMRI signal, we use three existing different machine learning methods: logistic regression (LR), support vector machine (SVM), and deep neural network (DNN) to achieve effective classification of different task states. The experiment compares the results of these machine learning methods and confirms that the deep neural network has the highest accuracy for task-state fMRI data classification and the effectiveness of the improved circle-EMD algorithm.

/pdf/classification-of-task-state-fmri-data-based-on-circle-emd-1non0z8krn.pdf

Classification of Task-State fMRI Data Based on Circle-EMD and Machine Learning.

Artificial intelligence, machine learning and deep learning in biomedical fields: A prospect in improvising medical healthcare systems

In order to classify glass artifacts into different categories based on two characteristics: the chemical composition of the sampling points and whether they are weathered or not, this paper is divided into two parts. In the first part, the entire dataset is divided using data preprocessing, and a logistic regression model is constructed for binary classification. The optimal parameters of the model are estimated using maximum likelihood estimation and gradient descent algorithm. The aim is to explore the classification patterns of the two types of glass artifacts. In the second part, unsupervised learning k-means algorithm is used for clustering. Indicators are selected based on mean square error and confidence level. The same model as in the first part is used to test the effectiveness of sub-classification. The results show that glass artifacts can be divided into two main categories: high-potassium glass and lead-barium glass, and further subdivided into 14 subcategories.

Research on classification of weathered glass based on logistic regression

The traditional logistic regression model is always used binary classification tasks, such as a person’s gender (male or female). In this paper, we introduce how to adapt the traditional logistic regression model to multiple classification task. To validate our proposed method, we conduct an experiment on a open dataset, and the experimental results show that our proposed method is promising in multiple classification task.

Multiple Classification Using Logistic Regression Model

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Multiple Classification Using Logistic Regression Model