154 research outputs found
A Scalable Deep Neural Network Architecture for Multi-Building and Multi-Floor Indoor Localization Based on Wi-Fi Fingerprinting
One of the key technologies for future large-scale location-aware services
covering a complex of multi-story buildings --- e.g., a big shopping mall and a
university campus --- is a scalable indoor localization technique. In this
paper, we report the current status of our investigation on the use of deep
neural networks (DNNs) for scalable building/floor classification and
floor-level position estimation based on Wi-Fi fingerprinting. Exploiting the
hierarchical nature of the building/floor estimation and floor-level
coordinates estimation of a location, we propose a new DNN architecture
consisting of a stacked autoencoder for the reduction of feature space
dimension and a feed-forward classifier for multi-label classification of
building/floor/location, on which the multi-building and multi-floor indoor
localization system based on Wi-Fi fingerprinting is built. Experimental
results for the performance of building/floor estimation and floor-level
coordinates estimation of a given location demonstrate the feasibility of the
proposed DNN-based indoor localization system, which can provide near
state-of-the-art performance using a single DNN, for the implementation with
lower complexity and energy consumption at mobile devices.Comment: 9 pages, 6 figure
Exploring Numerical Priors for Low-Rank Tensor Completion with Generalized CP Decomposition
Tensor completion is important to many areas such as computer vision, data
analysis, and signal processing. Enforcing low-rank structures on completed
tensors, a category of methods known as low-rank tensor completion has recently
been studied extensively. While such methods attained great success, none
considered exploiting numerical priors of tensor elements. Ignoring numerical
priors causes loss of important information regarding the data, and therefore
prevents the algorithms from reaching optimal accuracy. This work attempts to
construct a new methodological framework called GCDTC (Generalized CP
Decomposition Tensor Completion) for leveraging numerical priors and achieving
higher accuracy in tensor completion. In this newly introduced framework, a
generalized form of CP Decomposition is applied to low-rank tensor completion.
This paper also proposes an algorithm known as SPTC (Smooth Poisson Tensor
Completion) for nonnegative integer tensor completion as an instantiation of
the GCDTC framework. A series of experiments on real-world data indicated that
SPTC could produce results superior in completion accuracy to current
state-of-the-arts.Comment: 11 pages, 4 figures, 3 pseudocode algorithms, and 1 tabl
Sparse metric learning via smooth optimization
Copyright © 2009 NIPS Foundation23rd Annual Conference on Advances in Neural Information Processing Systems (NIPS 2009), Vancouver, Canada, 7-10 December 2009In this paper we study the problem of learning a low-rank (sparse) distance matrix. We propose a novel metric learning model which can simultaneously conduct dimension reduction and learn a distance matrix. The sparse representation involves a mixed-norm regularization which is non-convex. We then show that it can be equivalently formulated as a convex saddle (min-max) problem. From this saddle representation, we develop an efficient smooth optimization approach [15] for sparse metric learning, although the learning model is based on a non-differentiable loss function. This smooth optimization approach has an optimal convergence rate of O(1=t2) for smooth problems where t is the iteration number. Finally, we run experiments to validate the effectiveness and efficiency of our sparse metric learning model on various datasets
Can Perturbations Help Reduce Investment Risks? Risk-Aware Stock Recommendation via Split Variational Adversarial Training
In the stock market, a successful investment requires a good balance between
profits and risks. Recently, stock recommendation has been widely studied in
quantitative investment to select stocks with higher return ratios for
investors. Despite the success in making profits, most existing recommendation
approaches are still weak in risk control, which may lead to intolerable paper
losses in practical stock investing. To effectively reduce risks, we draw
inspiration from adversarial perturbations and propose a novel Split
Variational Adversarial Training (SVAT) framework for risk-aware stock
recommendation. Essentially, SVAT encourages the model to be sensitive to
adversarial perturbations of risky stock examples and enhances the model's risk
awareness by learning from perturbations. To generate representative
adversarial examples as risk indicators, we devise a variational perturbation
generator to model diverse risk factors. Particularly, the variational
architecture enables our method to provide a rough risk quantification for
investors, showing an additional advantage of interpretability. Experiments on
three real-world stock market datasets show that SVAT effectively reduces the
volatility of the stock recommendation model and outperforms state-of-the-art
baseline methods by more than 30% in terms of risk-adjusted profits
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