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 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 Unsupervised Deep Video Hashing with Balanced Rotation

2017 ◽  
Author(s):  
Gengshen Wu ◽  
Li Liu ◽  
Yuchen Guo ◽  
Guiguang Ding ◽  
Jungong Han ◽  
...  
Keyword(s):  
State Of The Art ◽  
Dimensional Space ◽  
Image Hashing ◽  
Neighborhood Structure ◽  
Function Learning ◽  
Video Hashing ◽  
Real World Datasets ◽  
Low Dimensional ◽  
Balanced Code ◽  
Hash Codes

Recently, hashing video contents for fast retrieval has received increasing attention due to the enormous growth of online videos. As the extension of image hashing techniques, traditional video hashing methods mainly focus on seeking the appropriate video features but pay little attention to how the video-specific features can be leveraged to achieve optimal binarization. In this paper, an end-to-end hashing framework, namely Unsupervised Deep Video Hashing (UDVH), is proposed, where feature extraction, balanced code learning and hash function learning are integrated and optimized in a self-taught manner. Particularly, distinguished from previous work, our framework enjoys two novelties: 1) an unsupervised hashing method that integrates the feature clustering and feature binarization, enabling the neighborhood structure to be preserved in the binary space; 2) a smart rotation applied to the video-specific features that are widely spread in the low-dimensional space such that the variance of dimensions can be balanced, thus generating more effective hash codes. Extensive experiments have been performed on two real-world datasets and the results demonstrate its superiority, compared to the state-of-the-art video hashing methods. To bootstrap further developments, the source code will be made publically available.

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 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 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.

scholarly journals A Probability Distribution and Location-aware ResNet Approach for QoS Prediction

2021 ◽  
Author(s):  
Wenyan Zhang ◽  
Ling Xu ◽  
Meng Yan ◽  
Ziliang Wang ◽  
Chunlei Fu
Keyword(s):  
Probability Distribution ◽  
State Of The Art ◽  
Cloud Platform ◽  
Qos Prediction ◽  
Location Aware ◽  
Single Feature ◽  
Nonlinear Features ◽  
Series Of Experiments ◽  
Low Dimensional ◽  
Better Than

In recent years, the number of online services has grown rapidly, invoking the required services through the cloud platform has become the primary trend. How to help users choose and recommend high-quality services among huge amounts of unused services has become a hot issue in research. Among the existing QoS prediction methods, the collaborative filtering (CF) method can only learn low-dimensional linear characteristics, and its effect is limited by sparse data. Although existing deep learning methods could capture high-dimensional nonlinear features better, most of them only use the single feature of identity, and the problem of network deepening gradient disappearance is serious, so the effect of QoS prediction is unsatisfactory. To address these problems, we propose an advanced probability distribution and location-aware ResNet approach for QoS Prediction (PLRes). This approach considers the historical invocations probability distribution and location characteristics of users and services, and first uses the ResNet in QoS prediction to reuses the features, which alleviates the problems of gradient disappearance and model degradation. A series of experiments are conducted on a real-world web service dataset WS-DREAM. At the density of 5%–30%, the experimental results on both QoS attribute response time and throughput indicate that PLRes performs better than the existing five state-of-the-art QoS prediction approaches.

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.

Vibration-Based Outlier Detection on High Dimensional Data

2016 ◽  
Vol 25 (03) ◽  
pp. 1650013
Author(s):  
Shuyin Xia ◽  
Guoyin Wang ◽  
Hong Yu ◽  
Qun Liu ◽  
Jin Wang
Keyword(s):  
Outlier Detection ◽  
Time Complexity ◽  
State Of The Art ◽  
High Dimensional Data ◽  
Difficult Problem ◽  
Index Structure ◽  
High Dimensional ◽  
Basic Model ◽  
Traditional Approaches ◽  
Better Than

Outlier detection is a difficult problem due to its time complexity being quadratic or cube in most cases, which makes it necessary to develop corresponding acceleration algorithms. Since the index structure (c.f. R tree) is used in the main acceleration algorithms, those approaches deteriorate when the dimensionality increases. In this paper, an approach named VBOD (vibration-based outlier detection) is proposed, in which the main variants assess the vibration. Since the basic model and approximation algorithm FASTVBOD do not need to compute the index structure, their performances are less sensitive to increasing dimensions than traditional approaches. The basic model of this approach has only quadratic time complexity. Furthermore, accelerated algorithms decrease time complexity to [Formula: see text]. The fact that this approach does not rely on any parameter selection is another advantage. FASTVBOD was compared with other state-of-the-art algorithms, and it performed much better than other methods especially on high dimensional data.

Methods for Binary Multidimensional Scaling

2002 ◽  
Vol 14 (5) ◽  
pp. 1195-1232 ◽  
Author(s):  
Douglas L. T. Rohde
Keyword(s):  
Neural Networks ◽  
Multidimensional Scaling ◽  
Dimensional Space ◽  
Discrete Space ◽  
High Dimensional ◽  
Continuous Space ◽  
Low Dimensional ◽  
Set Of Points ◽  
Lower Dimensional Space ◽  
Lower Dimensional

Multidimensional scaling (MDS) is the process of transforming a set of points in a high-dimensional space to a lower-dimensional one while preserving the relative distances between pairs of points. Although effective methods have been developed for solving a variety of MDS problems, they mainly depend on the vectors in the lower-dimensional space having real-valued components. For some applications, the training of neural networks in particular, it is preferable or necessary to obtain vectors in a discrete, binary space. Unfortunately, MDS into a low-dimensional discrete space appears to be a significantly harder problem than MDS into a continuous space. This article introduces and analyzes several methods for performing approximately optimized binary MDS.

scholarly journals Heterogeneous multidimensional scaling for complex networks

2015 ◽  
Vol 26 (02) ◽  
pp. 1550023 ◽  
Author(s):  
Qi Xuan ◽  
Xiaodi Ma ◽  
Chenbo Fu ◽  
Hui Dong ◽  
Guijun Zhang ◽  
...  
Keyword(s):  
Multidimensional Scaling ◽  
Real World ◽  
Practical Importance ◽  
Dimensional Euclidean Space ◽  
Euclidean Spaces ◽  
Scaling Method ◽  
Network Distance ◽  
Low Dimensional ◽  
Treat All ◽  
Better Than

Many real-world networks are essentially heterogeneous, where the nodes have different abilities to gain connections. Such networks are difficult to be embedded into low-dimensional Euclidean space if we ignore the heterogeneity and treat all the nodes equally. In this paper, based on a newly defined heterogeneous distance and a generalized network distance under the constraints of network and triangle inequalities, respectively, we propose a new heterogeneous multidimensional scaling method (HMDS) to embed different networks into proper Euclidean spaces. We find that HMDS behaves much better than the traditional multidimensional scaling method (MDS) in embedding different artificial and real-world networks into Euclidean spaces. Besides, we also propose a method to estimate the appropriate dimensions of Euclidean spaces for different networks, and find that the estimated dimensions are quite close to the real dimensions for those geometrical networks under study. These methods thus can help to better understand the evolution of real-world networks, and have practical importance in network visualization, community detection, link prediction and localization of wireless sensors.

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