ºÝºÝߣshows by User: panisson / http://www.slideshare.net/images/logo.gif ºÝºÝߣshows by User: panisson / Sun, 09 Apr 2017 10:41:52 GMT ºÝºÝߣShare feed for ºÝºÝߣshows by User: panisson TENSOR DECOMPOSITION WITH PYTHON /slideshow/tensor-decomposition-with-python/74759823 pycon8-firenze-170409104152
LEARNING STRUCTURES FROM MULTIDIMENSIONAL DATA Presentation at Pycon8, Florence, April 9 2017]]>
LEARNING STRUCTURES FROM MULTIDIMENSIONAL DATA Presentation at Pycon8, Florence, April 9 2017]]> Sun, 09 Apr 2017 10:41:52 GMT /slideshow/tensor-decomposition-with-python/74759823 panisson@slideshare.net(panisson) TENSOR DECOMPOSITION WITH PYTHON panisson LEARNING STRUCTURES FROM MULTIDIMENSIONAL DATA Presentation at Pycon8, Florence, April 9 2017 <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pycon8-firenze-170409104152-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> LEARNING STRUCTURES FROM MULTIDIMENSIONAL DATA Presentation at Pycon8, Florence, April 9 2017
TENSOR DECOMPOSITION WITH PYTHON from Andr辿 Panisson
]]> 10309 10 https://cdn.slidesharecdn.com/ss_thumbnails/pycon8-firenze-170409104152-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 Exploring temporal graph data with Python: 
a study on tensor decomposition of wearable sensor data (PyData NYC 2015) /slideshow/exploring-temporal-graph-data-with-python-a-study-on-tensor-decomposition-of-wearable-sensor-data/55356154 pydatanyc2015-151121021701-lva1-app6892
Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures. The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications.]]>
Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures. The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications.]]> Sat, 21 Nov 2015 02:17:01 GMT /slideshow/exploring-temporal-graph-data-with-python-a-study-on-tensor-decomposition-of-wearable-sensor-data/55356154 panisson@slideshare.net(panisson) Exploring temporal graph data with Python: 
a study on tensor decomposition of wearable sensor data (PyData NYC 2015) panisson Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures. The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications. <img style="border:1px solid #C3E6D8;float:right;" alt="" src="https://cdn.slidesharecdn.com/ss_thumbnails/pydatanyc2015-151121021701-lva1-app6892-thumbnail.jpg?width=120&amp;height=120&amp;fit=bounds" /><br> Tensor decompositions have gained a steadily increasing popularity in data mining applications. Data sources from sensor networks and Internet-of-Things applications promise a wealth of interaction data that can be naturally represented as multidimensional structures such as tensors. For example, time-varying social networks collected from wearable proximity sensors can be represented as 3-way tensors. By representing this data as tensors, we can use tensor decomposition to extract community structures with their structural and temporal signatures. The current standard framework for working with tensors, however, is Matlab. We will show how tensor decompositions can be carried out using Python, how to obtain latent components and how they can be interpreted, and what are some applications of this technique in the academy and industry. We will see a use case where a Python implementation of tensor decomposition is applied to a dataset that describes social interactions of people, collected using the SocioPatterns platform. This platform was deployed in different settings such as conferences, schools and hospitals, in order to support mathematical modelling and simulation of airborne infectious diseases. Tensor decomposition has been used in these scenarios to solve different types of problems: it can be used for data cleaning, where time-varying graph anomalies can be identified and removed from data; it can also be used to assess the impact of latent components in the spreading of a disease, and to devise intervention strategies that are able to reduce the number of infection cases in a school or hospital. These are just a few examples that show the potential of this technique in data mining and machine learning applications.
Exploring temporal graph data with Python: a study on tensor decomposition of wearable sensor data (PyData NYC 2015) from Andr辿 Panisson
]]> 5357 9 https://cdn.slidesharecdn.com/ss_thumbnails/pydatanyc2015-151121021701-lva1-app6892-thumbnail.jpg?width=120&height=120&fit=bounds presentation Black http://activitystrea.ms/schema/1.0/post http://activitystrea.ms/schema/1.0/posted 0 https://cdn.slidesharecdn.com/profile-photo-panisson-48x48.jpg?cb=1523053452 André Panisson is a Principal Researcher at the Data Science Laboratory of the Institute for Scientific Interchange in Turin, Italy. He received his PhD in Computer science from the University of Turin (Italy) in February 2012, and his MSc in Computer Science from the Federal University of Rio Grande do Sul (Brazil) in 2007. As a researcher, he contributed to the SocioPatterns project, and contributed to industrial research collaborations in the areas of finance and healthcare for risk and behavioural analysis, with companies based in Italy and in the US. His current research focuses on the intersection between the areas of Machine Learning, Network Science and Data Science, mainly in th... https://cdn.slidesharecdn.com/ss_thumbnails/pycon8-firenze-170409104152-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/tensor-decomposition-with-python/74759823 TENSOR DECOMPOSITION W... https://cdn.slidesharecdn.com/ss_thumbnails/pydatanyc2015-151121021701-lva1-app6892-thumbnail.jpg?width=320&height=320&fit=bounds slideshow/exploring-temporal-graph-data-with-python-a-study-on-tensor-decomposition-of-wearable-sensor-data/55356154 Exploring temporal gra...