1,626 research outputs found
Fourier PCA and Robust Tensor Decomposition
Fourier PCA is Principal Component Analysis of a matrix obtained from higher
order derivatives of the logarithm of the Fourier transform of a
distribution.We make this method algorithmic by developing a tensor
decomposition method for a pair of tensors sharing the same vectors in rank-
decompositions. Our main application is the first provably polynomial-time
algorithm for underdetermined ICA, i.e., learning an matrix
from observations where is drawn from an unknown product
distribution with arbitrary non-Gaussian components. The number of component
distributions can be arbitrarily higher than the dimension and the
columns of only need to satisfy a natural and efficiently verifiable
nondegeneracy condition. As a second application, we give an alternative
algorithm for learning mixtures of spherical Gaussians with linearly
independent means. These results also hold in the presence of Gaussian noise.Comment: Extensively revised; details added; minor errors corrected;
exposition improve
Negative Link Prediction in Social Media
Signed network analysis has attracted increasing attention in recent years.
This is in part because research on signed network analysis suggests that
negative links have added value in the analytical process. A major impediment
in their effective use is that most social media sites do not enable users to
specify them explicitly. In other words, a gap exists between the importance of
negative links and their availability in real data sets. Therefore, it is
natural to explore whether one can predict negative links automatically from
the commonly available social network data. In this paper, we investigate the
novel problem of negative link prediction with only positive links and
content-centric interactions in social media. We make a number of important
observations about negative links, and propose a principled framework NeLP,
which can exploit positive links and content-centric interactions to predict
negative links. Our experimental results on real-world social networks
demonstrate that the proposed NeLP framework can accurately predict negative
links with positive links and content-centric interactions. Our detailed
experiments also illustrate the relative importance of various factors to the
effectiveness of the proposed framework
Triggering information by context
With the increased availability of personal computers with attached sensors to capture their environment, there is a big opportunity for context-aware applications; these automatically provide information and/or take actions according to the user's present context, as detected by sensors. When wel l designed, these applications provide an opportunity to tailor the provision of information closely to the user's current needs. A sub-set of context-a ware applications are discrete applications, where discrete pieces of i nformation are attached to individual contexts, to be triggered when the user enters those contexts. The advantage of discrete applications is that authori ng them can be solely a creative process rather than a programming process: it can be a task akin to creating simple web pages. This paper looks at a general system that can be used in any discrete context- aware application. It propounds a general triggering rule, and investigates how this rule applies in practical applications
Smoothed Analysis of Tensor Decompositions
Low rank tensor decompositions are a powerful tool for learning generative
models, and uniqueness results give them a significant advantage over matrix
decomposition methods. However, tensors pose significant algorithmic challenges
and tensors analogs of much of the matrix algebra toolkit are unlikely to exist
because of hardness results. Efficient decomposition in the overcomplete case
(where rank exceeds dimension) is particularly challenging. We introduce a
smoothed analysis model for studying these questions and develop an efficient
algorithm for tensor decomposition in the highly overcomplete case (rank
polynomial in the dimension). In this setting, we show that our algorithm is
robust to inverse polynomial error -- a crucial property for applications in
learning since we are only allowed a polynomial number of samples. While
algorithms are known for exact tensor decomposition in some overcomplete
settings, our main contribution is in analyzing their stability in the
framework of smoothed analysis.
Our main technical contribution is to show that tensor products of perturbed
vectors are linearly independent in a robust sense (i.e. the associated matrix
has singular values that are at least an inverse polynomial). This key result
paves the way for applying tensor methods to learning problems in the smoothed
setting. In particular, we use it to obtain results for learning multi-view
models and mixtures of axis-aligned Gaussians where there are many more
"components" than dimensions. The assumption here is that the model is not
adversarially chosen, formalized by a perturbation of model parameters. We
believe this an appealing way to analyze realistic instances of learning
problems, since this framework allows us to overcome many of the usual
limitations of using tensor methods.Comment: 32 pages (including appendix
Background of nanoscale gradient formation in the structure of steel 9HF at hard plastic deformation
Non-Redundant Spectral Dimensionality Reduction
Spectral dimensionality reduction algorithms are widely used in numerous
domains, including for recognition, segmentation, tracking and visualization.
However, despite their popularity, these algorithms suffer from a major
limitation known as the "repeated Eigen-directions" phenomenon. That is, many
of the embedding coordinates they produce typically capture the same direction
along the data manifold. This leads to redundant and inefficient
representations that do not reveal the true intrinsic dimensionality of the
data. In this paper, we propose a general method for avoiding redundancy in
spectral algorithms. Our approach relies on replacing the orthogonality
constraints underlying those methods by unpredictability constraints.
Specifically, we require that each embedding coordinate be unpredictable (in
the statistical sense) from all previous ones. We prove that these constraints
necessarily prevent redundancy, and provide a simple technique to incorporate
them into existing methods. As we illustrate on challenging high-dimensional
scenarios, our approach produces significantly more informative and compact
representations, which improve visualization and classification tasks
The occurrence of wakefieldite, a rare earth element vanadate, in the rhyolitic Joe Lott Tuff, Utah, USA
The high-silica rhyolitic Joe Lott Tuff was erupted at 19.2 ± 0.4 Ma from the Mount Belknap caldera, SW Utah. Certain units in the tuff contain two species of wakefieldite, the Nd- A nd Y-dominant types. They occur in disseminated streaks and patches in association with rhodochrosite, calcite, Fe oxide, cerite-(Ce), and a Mn silicate (caryopilite?), thought to have been deposited from hydrothermal fluids. The wakefieldites contain the highest levels of As (≤15.34 wt.% As2O5) and P (≤5.7 wt.% P2O5) yet recorded in this mineral, indicating significant solid solution towards chernovite-(Y) and xenotime-(Y). Thorium levels are also unusually high (≤14.2 wt.% ThO2). The source of the hydrothermal fluid(s) is unknown but might be related to uranium mineralisation in the region, in that As, V and U are commonly associated in such deposits. © 2019 Mineralogical Society of Great Britain and Ireland
Diffusion methods for wind power ramp detection
The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-642-38679-4_9Proceedings of 12th International Work-Conference on Artificial Neural Networks, IWANN 2013, Puerto de la Cruz, Tenerife, Spain, June 12-14, 2013, Part IThe prediction and management of wind power ramps is currently receiving large attention as it is a crucial issue for both system operators and wind farm managers. However, this is still an issue far from being solved and in this work we will address it as a classification problem working with delay vectors of the wind power time series and applying local Mahalanobis K-NN search with metrics derived from Anisotropic Diffusion methods. The resulting procedures clearly outperform a random baseline method and yield good sensitivity but more work is needed to improve on specificity and, hence, precision.With partial support from Spain's grant TIN2010-21575-
C02-01 and the UAM-ADIC Chair for Machine Learning. The rst author is also
supported by an FPI-UAM grant and kindly thanks the Applied Mathematics
Department of Yale University for receiving her during her visits. The second
author is supported by the FPU-MEC grant AP2008-00167
Personalisation and recommender systems in digital libraries
Widespread use of the Internet has resulted in digital libraries that are increasingly used by diverse communities of users for diverse purposes and in which sharing and collaboration have become important social elements. As such libraries become commonplace, as their contents and services become more varied, and as their patrons become more experienced with computer technology, users will expect more sophisticated services from these libraries. A simple search function, normally an integral part of any digital library, increasingly leads to user frustration as user needs become more complex and as the volume of managed information increases. Proactive digital libraries, where the library evolves from being passive and untailored, are seen as offering great potential for addressing and overcoming these issues and include techniques such as personalisation and recommender systems. In this paper, following on from the DELOS/NSF Working Group on Personalisation and Recommender Systems for Digital Libraries, which met and reported during 2003, we present some background material on the scope of personalisation and recommender systems in digital libraries. We then outline the working group’s vision for the evolution of digital libraries and the role that personalisation and recommender systems will play, and we present a series of research challenges and specific recommendations and research priorities for the field
User interfaces for information systems
This paper presents descriptions of four information-system interface projects in progress at ESRIN, each demonstrating a somewhat different approach to interface design, but ali sharing the commonality of responding to user goals, tasks and characteristics. It is suggested that next-generation scientific information systems will have to be designed for direct access by end users to a large variety of information sources, through a commom interface. Design of such systems, including their interfaces, should be based on a multi-level analysis of user goals, tasks and domain views.Se describen cuatro proyectos de interfaces de sistemas de información que se están desarrollando en ESRIN (establecimiento de la Agencia Espacial Europea, en Frascati). Cada uno de ellos muestra un enfoque diferente del diseño de interfaces, pero todos tienen en común el responder a los objetivos, tareas y características de los usuarios. Se sugiere que la próxima generación de sistemas de información científica se tendrá que diseñar para permitir el acceso directo de los usuarios finales a una gran variedad de fuentes de información a través de una interfaz común. El diseño de tales sistemas y de sus interfaces debería basarse en un análisis multinivel de objetivos, tareas y puntos de vista propios de la materia de trabajo de cada usuario
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