Xin Bi

Bio: Xin Bi is an academic researcher from Chinese Ministry of Education. The author has contributed to research in topics: Extreme learning machine & XML. The author has an hindex of 8, co-authored 32 publications receiving 246 citations. Previous affiliations of Xin Bi include Northeastern University (China) & Northeastern University.

Functional Brain Network Classification for Alzheimer’s Disease Detection with Deep Features and Extreme Learning Machine

Abstract: The human brain can be inherently modeled as a brain network, where nodes denote billions of neurons and edges denote massive connections between neurons. Analysis on functional brain networks provides powerful abilities to discover potential mechanisms of human brain, and to aid brain disease detection, such as AD (Alzheimer’s disease). Effective discrimination of patients of AD and MCI (mild cognitive impairment) from NC (normal control) is important for the early diagnosis of AD. Therefore, this paper explores the problem of brain network classification for AD detection. Two deep learning methods of functional brain network classification are designed. The convolutional learning method learns the deep regional-connectivity features, while the recurrent learning method learns the deep adjacent positional features. The ELM (extreme learning machine)-boosted structure is also implemented to further improve the learning ability. Extensive experiments are conducted to evaluate and compare the AUC (area under curve), accuracy, recall, and training time of the proposed methods on a real-world dataset. Results indicate that (1) the proposed methods which learn deep features directly from brain networks outperform shallow learning methods and (2) models with the ELM-boosted structure achieve a higher performance. This paper explores the brain networks learning with deep features and ELM. The results demonstrate that the proposed methods provide a satisfactory learning ability in the application of AD detection.

LDPart: Effective Location-Record Data Publication via Local Differential Privacy

Abstract: Driven by the advance of positioning technology and the tremendous popularity of location-based services, location-record data have become unprecedentedly available. Publishing such data is of vital importance to the advancement of a wide spectrum of applications, such as marketing analysis, targeted advertising, and urban planning. However, the data collection may pose considerable threats to the individuals privacy. Local differential privacy (LDP) has recently emerged as a strong privacy standard for collecting sensitive information from users. Due to the inherent high dimensionality, it is particularly challenging to publish the location-record data under LDP. In this paper, we propose LDPart , a probabilistic top-down partitioning algorithm to effectively generate a sanitized location-record data. Our approach employs a carefully designed partition tree model to extract essential information in terms of location records. Furthermore, it also makes use of a novel adaptive user allocation scheme and a series of optimization techniques to improve the accuracy of the released data. The extensive experiments conducted on real-world datasets demonstrate that the proposed approach maintains high utility while providing privacy guarantees.

Deep Learning in Mining Biological Data

Abstract: Recent technological advancements in data acquisition tools allowed life scientists to acquire multimodal data from different biological application domains. Categorized in three broad types (i.e. images, signals, and sequences), these data are huge in amount and complex in nature. Mining such enormous amount of data for pattern recognition is a big challenge and requires sophisticated data-intensive machine learning techniques. Artificial neural network-based learning systems are well known for their pattern recognition capabilities, and lately their deep architectures—known as deep learning (DL)—have been successfully applied to solve many complex pattern recognition problems. To investigate how DL—especially its different architectures—has contributed and been utilized in the mining of biological data pertaining to those three types, a meta-analysis has been performed and the resulting resources have been critically analysed. Focusing on the use of DL to analyse patterns in data from diverse biological domains, this work investigates different DL architectures’ applications to these data. This is followed by an exploration of available open access data sources pertaining to the three data types along with popular open-source DL tools applicable to these data. Also, comparative investigations of these tools from qualitative, quantitative, and benchmarking perspectives are provided. Finally, some open research challenges in using DL to mine biological data are outlined and a number of possible future perspectives are put forward.

A Parallel Multiclassification Algorithm for Big Data Using an Extreme Learning Machine

Abstract: As data sets become larger and more complicated, an extreme learning machine (ELM) that runs in a traditional serial environment cannot realize its ability to be fast and effective. Although a parallel ELM (PELM) based on MapReduce to process large-scale data shows more efficient learning speed than identical ELM algorithms in a serial environment, some operations, such as intermediate results stored on disks and multiple copies for each task, are indispensable, and these operations create a large amount of extra overhead and degrade the learning speed and efficiency of the PELMs. In this paper, an efficient ELM based on the Spark framework (SELM), which includes three parallel subalgorithms, is proposed for big data classification. By partitioning the corresponding data sets reasonably, the hidden layer output matrix calculation algorithm, matrix $\mathbf {\hat {U}}$ decomposition algorithm, and matrix $\mathbf {V}$ decomposition algorithm perform most of the computations locally. At the same time, they retain the intermediate results in distributed memory and cache the diagonal matrix as broadcast variables instead of several copies for each task to reduce a large amount of the costs, and these actions strengthen the learning ability of the SELM. Finally, we implement our SELM algorithm to classify large data sets. Extensive experiments have been conducted to validate the effectiveness of the proposed algorithms. As shown, our SELM achieves an $8.71\times$ speedup on a cluster with ten nodes, and reaches a $13.79\times$ speedup with 15 nodes, an $18.74\times$ speedup with 20 nodes, a $23.79\times$ speedup with 25 nodes, a $28.89\times$ speedup with 30 nodes, and a $33.81\times$ speedup with 35 nodes.

Deep Computation Model for Unsupervised Feature Learning on Big Data

Abstract: Deep learning has been successfully applied to feature learning in speech recognition, image classification and language processing. However, current deep learning models work in the vector space, resulting in the failure to learn features for big data since a vector cannot model the highly non-linear distribution of big data, especially heterogeneous data. This paper proposes a deep computation model for feature learning on big data, which uses a tensor to model the complex correlations of heterogeneous data. To fully learn the underlying data distribution, the proposed model uses the tensor distance as the average sum-of-squares error term of the reconstruction error in the output layer. To train the parameters of the proposed model, the paper designs a high-order back-propagation algorithm (HBP) by extending the conventional back-propagation algorithm from the vector space to the high-order tensor space. To evaluate the performance of the proposed model, we carried out the experiments on four representative datasets by comparison with stacking auto-encoders and multimodal deep learning models. Experimental results clearly demonstrate that the proposed model is efficient to perform feature learning when evaluated using the STL-10, CUAVE, SANE and INEX datasets.