A theoretical result of sparse signal recovery via alternating projection method

Liu, Haifeng; Peng, Jigen; Lin, Zhipeng

doi:10.1016/j.ins.2019.08.001

Cited by 14 publications

(7 citation statements)

References 19 publications

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“…CS is mainly used in the field of signal recovery [39–42]. Candés and Tao [15] proved that the CS problem can be solved by minimizing ℓ 0 norm model:

$$\begin{equation} {\begin{cases} \min \Vert {\mathbf {x}}\Vert _0 \\[3pt] \text{s.t.}

…”

Section: Related Workmentioning

confidence: 99%

SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition

Yang

Zhang

et al. 2022

IET Image Processing

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Although most state‐of‐the‐art action recognition models have adopted a two‐stream 3D convolutional structure as a backbone network, few works have studied the impact of loss functions on action recognition models. In addition, sparsity is used as a key prior knowledge in many fields. However, as far as is known, no one has studied the influence of the sparsity of network output on the output of deep learning‐based action recognition models. Therefore, this paper proposes a novel two‐stream inflated 3D ConvNet based on the sparse regularization (SRI3D) model for action recognition. In order to allow the network to learn the sparsity of output, the ℓ1 norm is embedded in the loss function in regularization form in a plug‐and‐play manner. It can make the classification result after the fusion of the two‐stream network only be the category with the highest confidence in one of the streams and not the other cases. The proposed loss function based on sparse regularization makes the output vector of the neural network as sparse as possible so that the classification results will not be ambiguous. Experimental results show that compared with other state‐of‐the‐art models, this SRI3D has a competitive advantage on Kinetics‐400, Something‐Something V2, UCF‐101 and HMDB‐51.

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“…CS is mainly used in the field of signal recovery [39–42]. Candés and Tao [15] proved that the CS problem can be solved by minimizing ℓ 0 norm model:

$$\begin{equation} {\begin{cases} \min \Vert {\mathbf {x}}\Vert _0 \\[3pt] \text{s.t.}

…”

Section: Related Workmentioning

confidence: 99%

SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition

Yang

Zhang

et al. 2022

IET Image Processing

View full text Add to dashboard Cite

show abstract

“…When a disaster occurs, social media users usually generate massive amounts of data on social media such as Sina Weibo, Facebook, and Twitter. These social media data with temporal and spatial attributes have become an important means of understanding public behavior [10]. Managers and researchers can analyze social media data for disaster detection [11,12], situational awareness [13], risk communication [14][15][16][17][18], intelligent decision-making [13], emergency response to public opinions [19,20], and post-disaster damage assessment [21][22][23][24][25].…”

Section: Content Analysis On Social Media In Disaster Managementmentioning

confidence: 99%

Temporal, Spatial, and Socioeconomic Dynamics in Social Media Thematic Emphases during Typhoon Mangkhut

Zhu

Liu

2021

Sustainability

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Disaster-related social media data often consist of several themes, and each theme allows people to understand and communicate from a certain perspective. It is necessary to take into consideration the dynamics of thematic emphases on social media in order to understand the nature of such data and to use them appropriately. This paper proposes a framework to analyze the temporal, spatial, and socioeconomic disparities in thematic emphases on social media during Typhoon Mangkhut. First, the themes were identified through a latent Dirichlet allocation model during Typhoon Mangkhut. Then, we adopted a quantitative method of indexing the themes to represent the dynamics of the thematic emphases. Spearman correlation analyses between the index and eight socioeconomic variables were conducted to identify the socioeconomic disparities in thematic emphases. The main research findings are revealing. From the perspective of time evolution, Theme 1 (general response) and Theme 2 (urban transportation) hold the principal position throughout the disaster. In the early hours of the disaster, Theme 3 (typhoon status and impact) was the most popular theme, but its popularity fell sharply soon after. From the perspective of spatial distribution, people in severely affected areas were more concerned about urban transportation (Theme 2), while people in moderately affected areas were more concerned about typhoon status and impact (Theme 3) and animals and humorous news (Theme 4). The results of the correlation analyses show that there are differences in thematic emphases across disparate socioeconomic groups. Women preferred to post about typhoon status and impact (Theme 3) and animals and humorous news (Theme 4), while people with higher income paid less attention to these two themes during Typhoon Mangkhut. These findings can help government agencies and other stakeholders address public needs effectively and accurately in disaster responses.

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“…These online social interactions leave digital footprints (spatiotemporal user-generated opinions, check-ins, photographs), which, once interpreted, are highly valuable for urban research purposes [21] and for informing decisionmaking processes. It has now been over a decade since the footprints generated by users of virtual social media platforms have proved to be useful for detecting key physical and behavioural aspects of the urban environment [22][23][24] and discerning phenomena that are hard to appreciate directly by the human senses: people s perceptual responses to the environment [25,26], the cultural diversity of an urban setting [27], and other complex non-physical phenomena, such as the sense of place [28] and the character and vibrancy of local urban life [29][30][31][32].…”

Section: Digital Footprints For the Study Of Urban Phenomenamentioning

confidence: 99%

Culturally Diverse Street-Level Urban Activities through the Lens of Digital Footprints

2021

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Acknowledging and sustaining the ethnic and cultural diversity that the phenomenon of globalization has brought to the urban environment is one of the target objectives to seek spatial and social sustainability in today’s cities. This study aims to build upon previous research approaches that sought to characterize culturally diverse, urban public spaces. For this purpose, a method that encompasses digital and physical layers of information has been proposed to gather signs of culturally diverse, street-level urban and economic activities. Geolocated data from three social media platforms, as well as field-work observations, were collected from two case study street segments with different socio-cultural, demographic profiles. The findings suggest that economic activities related to retail and restaurants, and especially those with higher levels of personalization that reflect “cultural specialisms”, have a significative relevance in both the physical and virtual domains. However, unlike the case study area with the higher socio-economic profile, the urban vibrancy observed through field work throughout the more culturally diverse street segment was not found to be fully represented by social media data. There is still much room for research on the extent to which these sources are useful for characterizing fine-grain street-scale phenomena.

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A theoretical result of sparse signal recovery via alternating projection method

Cited by 14 publications

References 19 publications

SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition

SRI3D: Two‐stream inflated 3D ConvNet based on sparse regularization for action recognition

Temporal, Spatial, and Socioeconomic Dynamics in Social Media Thematic Emphases during Typhoon Mangkhut

Culturally Diverse Street-Level Urban Activities through the Lens of Digital Footprints

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