scholarly journals Learning Concise Representations of Users' Influences through Online Behaviors

2017 ◽  
Author(s):  
Shenghua Liu ◽  
Houdong Zheng ◽  
Huawei Shen ◽  
Xueqi Cheng ◽  
Xiangwen Liao
Keyword(s):  
State Of The Art ◽  
Fundamental Problem ◽  
Viral Marketing ◽  
High Dimensional ◽  
Opinion Formation ◽  
Distributed Representations ◽  
Interpersonal Influences ◽  
Low Dimensional ◽  
Independent Model ◽  
Do So

Whereas it is well known that social network users influence each other, a fundamental problem in influence maximization, opinion formation and viral marketing is that users' influences are difficult to quantify. Previous work has directly defined an independent model parameter to capture the interpersonal influence between each pair of users. However, such models do not consider how influences depend on each other if they originate from the same user or if they act on the same user. To do so, these models need a parameter for each pair of users, which results in high-dimensional models becoming easily trapped into the overfitting problem. Given these problems, another way of defining the parameters is needed to consider the dependencies. Thus we propose a model that defines parameters for every user with a latent influence vector and a susceptibility vector. Such low-dimensional and distributed representations naturally cause the interpersonal influences involving the same user to be coupled with each other, thus reducing the model's complexity. Additionally, the model can easily consider the sentimental polarities of users' messages and how sentiment affects users' influences. In this study, we conduct extensive experiments on real Microblog data, showing that our model with distributed representations achieves better accuracy than the state-of-the-art and pair-wise models, and that learning influences on sentiments benefit performance.

scholarly journals Subspace Clustering with Sparsity and Grouping Effect

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Binbin Zhang ◽  
Weiwei Wang ◽  
Xiangchu Feng
Keyword(s):  
State Of The Art ◽  
Subspace Clustering ◽  
The Self ◽  
Dimensional Structure ◽  
High Dimensional ◽  
Affinity Matrix ◽  
Grouping Effect ◽  
Art Methods ◽  
Data Points ◽  
Low Dimensional

Subspace clustering aims to group a set of data from a union of subspaces into the subspace from which it was drawn. It has become a popular method for recovering the low-dimensional structure underlying high-dimensional dataset. The state-of-the-art methods construct an affinity matrix based on the self-representation of the dataset and then use a spectral clustering method to obtain the final clustering result. These methods show that sparsity and grouping effect of the affinity matrix are important in recovering the low-dimensional structure. In this work, we propose a weighted sparse penalty and a weighted grouping effect penalty in modeling the self-representation of data points. The experimental results on Extended Yale B, USPS, and Berkeley 500 image segmentation datasets show that the proposed model is more effective than state-of-the-art methods in revealing the subspace structure underlying high-dimensional dataset.

scholarly journals SCDRHA: A scRNA-Seq Data Dimensionality Reduction Algorithm Based on Hierarchical Autoencoder

2021 ◽  
Vol 12 ◽  
Author(s):  
Jianping Zhao ◽  
Na Wang ◽  
Haiyun Wang ◽  
Chunhou Zheng ◽  
Yansen Su
Keyword(s):  
Dimensionality Reduction ◽  
Data Visualization ◽  
State Of The Art ◽  
Dimensional Space ◽  
High Dimensional ◽  
Reduction Algorithm ◽  
Cell Clustering ◽  
Data Dimensionality Reduction ◽  
Single Cell Rna Sequencing ◽  
Low Dimensional

Dimensionality reduction of high-dimensional data is crucial for single-cell RNA sequencing (scRNA-seq) visualization and clustering. One prominent challenge in scRNA-seq studies comes from the dropout events, which lead to zero-inflated data. To address this issue, in this paper, we propose a scRNA-seq data dimensionality reduction algorithm based on a hierarchical autoencoder, termed SCDRHA. The proposed SCDRHA consists of two core modules, where the first module is a deep count autoencoder (DCA) that is used to denoise data, and the second module is a graph autoencoder that projects the data into a low-dimensional space. Experimental results demonstrate that SCDRHA has better performance than existing state-of-the-art algorithms on dimension reduction and noise reduction in five real scRNA-seq datasets. Besides, SCDRHA can also dramatically improve the performance of data visualization and cell clustering.

scholarly journals Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 389
Author(s):  
Sonali Parbhoo ◽  
Mario Wieser ◽  
Aleksander Wieczorek ◽  
Volker Roth
Keyword(s):  
Treatment Effects ◽  
State Of The Art ◽  
Relevant Information ◽  
High Dimensional ◽  
Second Step ◽  
Test Time ◽  
Healthcare Applications ◽  
Information Bottleneck ◽  
Covariate Information ◽  
Low Dimensional

Estimating the effects of an intervention from high-dimensional observational data is a challenging problem due to the existence of confounding. The task is often further complicated in healthcare applications where a set of observations may be entirely missing for certain patients at test time, thereby prohibiting accurate inference. In this paper, we address this issue using an approach based on the information bottleneck to reason about the effects of interventions. To this end, we first train an information bottleneck to perform a low-dimensional compression of covariates by explicitly considering the relevance of information for treatment effects. As a second step, we subsequently use the compressed covariates to perform a transfer of relevant information to cases where data are missing during testing. In doing so, we can reliably and accurately estimate treatment effects even in the absence of a full set of covariate information at test time. Our results on two causal inference benchmarks and a real application for treating sepsis show that our method achieves state-of-the-art performance, without compromising interpretability.

Data synthesis via differentially private markov random fields

2021 ◽  
Vol 14 (11) ◽  
pp. 2190-2202
Author(s):  
Kuntai Cai ◽  
Xiaoyu Lei ◽  
Jianxin Wei ◽  
Xiaokui Xiao
Keyword(s):  
Markov Random Fields ◽  
Input Data ◽  
Differential Privacy ◽  
State Of The Art ◽  
Synthetic Data ◽  
The State ◽  
High Dimensional ◽  
Data Synthesis ◽  
Markov Random ◽  
Low Dimensional

This paper studies the synthesis of high-dimensional datasets with differential privacy (DP). The state-of-the-art solution addresses this problem by first generating a set M of noisy low-dimensional marginals of the input data D , and then use them to approximate the data distribution in D for synthetic data generation. However, it imposes several constraints on M that considerably limits the choices of marginals. This makes it difficult to capture all important correlations among attributes, which in turn degrades the quality of the resulting synthetic data. To address the above deficiency, we propose PrivMRF, a method that (i) also utilizes a set M of low-dimensional marginals for synthesizing high-dimensional data with DP, but (ii) provides a high degree of flexibility in the choices of marginals. The key idea of PrivMRF is to select an appropriate M to construct a Markov random field (MRF) that models the correlations among the attributes in the input data, and then use the MRF for data synthesis. Experimental results on four benchmark datasets show that PrivMRF consistently outperforms the state of the art in terms of the accuracy of counting queries and classification tasks conducted on the synthetic data generated.

scholarly journals Robust Video Hashing Based on Multidimensional Scaling and Ordinal Measures

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Zhenjun Tang ◽  
Shaopeng Zhang ◽  
Zhenhai Chen ◽  
Xianquan Zhang
Keyword(s):  
Multidimensional Scaling ◽  
State Of The Art ◽  
High Dimensional ◽  
Discrete Wavelet ◽  
Dimensional Matrix ◽  
Video Hashing ◽  
Multimedia Search ◽  
Low Dimensional ◽  
Low Dimensional Features ◽  
Better Than

Multimedia hashing is a useful technology of multimedia management, e.g., multimedia search and multimedia security. This paper proposes a robust multimedia hashing for processing videos. The proposed video hashing constructs a high-dimensional matrix via gradient features in the discrete wavelet transform (DWT) domain of preprocessed video, learns low-dimensional features from high-dimensional matrix via multidimensional scaling, and calculates video hash by ordinal measures of the learned low-dimensional features. Extensive experiments on 8300 videos are performed to examine the proposed video hashing. Performance comparisons reveal that the proposed scheme is better than several state-of-the-art schemes in balancing the performances of robustness and discrimination.

scholarly journals Self-Guided Evolution Strategies with Historical Estimated Gradients

2020 ◽  
Author(s):  
Fei-Yu Liu ◽  
Zi-Niu Li ◽  
Chao Qian
Keyword(s):  
State Of The Art ◽  
Dimensional Subspace ◽  
Black Box ◽  
Evolution Strategies ◽  
Superior Performance ◽  
High Dimensional ◽  
Parameter Perturbations ◽  
Low Dimensional ◽  
Dimensional Optimization ◽  
Search Directions

Evolution Strategies (ES) are a class of black-box optimization algorithms and have been widely applied to solve problems, e.g., in reinforcement learning (RL), where the true gradient is unavailable. ES estimate the gradient of an objective function with respect to the parameters by randomly sampling search directions and evaluating parameter perturbations in these directions. However, the gradient estimator of ES tends to have a high variance for high-dimensional optimization, thus requiring a large number of samples and making ES inefficient. In this paper, we propose a new ES algorithm SGES, which utilizes historical estimated gradients to construct a low-dimensional subspace for sampling search directions, and adjusts the importance of this subspace adaptively. We prove that the variance of the gradient estimator of SGES can be much smaller than that of Vanilla ES; meanwhile, its bias can be well bounded. Empirical results on benchmark black-box functions and a set of popular RL tasks exhibit the superior performance of SGES over state-of-the-art ES algorithms.

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

scholarly journals Classification of Brainwaves for Sleep Stages by High-Dimensional FFT Features from EEG Signals

2020 ◽  
Vol 10 (5) ◽  
pp. 1797 ◽  
Author(s):  
Mera Kartika Delimayanti ◽  
Bedy Purnama ◽  
Ngoc Giang Nguyen ◽  
Mohammad Reza Faisal ◽  
Kunti Robiatul Mahmudah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sleep Stage ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Sleep Stages ◽  
Eeg Signals ◽  
Stage Classification ◽  
Sleep Stage Classification ◽  
Low Dimensional

Manual classification of sleep stage is a time-consuming but necessary step in the diagnosis and treatment of sleep disorders, and its automation has been an area of active study. The previous works have shown that low dimensional fast Fourier transform (FFT) features and many machine learning algorithms have been applied. In this paper, we demonstrate utilization of features extracted from EEG signals via FFT to improve the performance of automated sleep stage classification through machine learning methods. Unlike previous works using FFT, we incorporated thousands of FFT features in order to classify the sleep stages into 2–6 classes. Using the expanded version of Sleep-EDF dataset with 61 recordings, our method outperformed other state-of-the art methods. This result indicates that high dimensional FFT features in combination with a simple feature selection is effective for the improvement of automated sleep stage classification.

scholarly journals TransET: Knowledge Graph Embedding with Entity Types

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1407
Author(s):  
Peng Wang ◽  
Jing Zhou ◽  
Yuzhang Liu ◽  
Xingchen Zhou
Keyword(s):  
Link Prediction ◽  
State Of The Art ◽  
Score Function ◽  
Graph Embedding ◽  
Vector Spaces ◽  
Knowledge Graph ◽  
Semantic Features ◽  
Knowledge Graphs ◽  
Real World Datasets ◽  
Low Dimensional

Knowledge graph embedding aims to embed entities and relations into low-dimensional vector spaces. Most existing methods only focus on triple facts in knowledge graphs. In addition, models based on translation or distance measurement cannot fully represent complex relations. As well-constructed prior knowledge, entity types can be employed to learn the representations of entities and relations. In this paper, we propose a novel knowledge graph embedding model named TransET, which takes advantage of entity types to learn more semantic features. More specifically, circle convolution based on the embeddings of entity and entity types is utilized to map head entity and tail entity to type-specific representations, then translation-based score function is used to learn the presentation triples. We evaluated our model on real-world datasets with two benchmark tasks of link prediction and triple classification. Experimental results demonstrate that it outperforms state-of-the-art models in most cases.

scholarly journals Efficient Rank-Based Diffusion Process with Assured Convergence

2021 ◽  
Vol 7 (3) ◽  
pp. 49
Author(s):  
Daniel Carlos Guimarães Pedronette ◽  
Lucas Pascotti Valem ◽  
Longin Jan Latecki
Keyword(s):  
Diffusion Process ◽  
Learning Strategies ◽  
State Of The Art ◽  
Representation Learning ◽  
Theoretical Background ◽  
High Dimensional ◽  
Visual Features ◽  
Learning Approaches ◽  
Previous Decade ◽  
Asymptotic Complexity

Visual features and representation learning strategies experienced huge advances in the previous decade, mainly supported by deep learning approaches. However, retrieval tasks are still performed mainly based on traditional pairwise dissimilarity measures, while the learned representations lie on high dimensional manifolds. With the aim of going beyond pairwise analysis, post-processing methods have been proposed to replace pairwise measures by globally defined measures, capable of analyzing collections in terms of the underlying data manifold. The most representative approaches are diffusion and ranked-based methods. While the diffusion approaches can be computationally expensive, the rank-based methods lack theoretical background. In this paper, we propose an efficient Rank-based Diffusion Process which combines both approaches and avoids the drawbacks of each one. The obtained method is capable of efficiently approximating a diffusion process by exploiting rank-based information, while assuring its convergence. The algorithm exhibits very low asymptotic complexity and can be computed regionally, being suitable to outside of dataset queries. An experimental evaluation conducted for image retrieval and person re-ID tasks on diverse datasets demonstrates the effectiveness of the proposed approach with results comparable to the state-of-the-art.

Sign in / Sign up

Export Citation Format

Share Document