410 research outputs found
Discriminative Appearance Models for Face Alignment
The proposed face alignment algorithm uses local gradient features as the appearance representation. These features are obtained by pixel value comparison, which provide robustness against changes in illumination, as well as partial occlusion and local deformation due to the locality. The adopted features are modeled in three discriminative methods, which correspond to different alignment cost functions. The discriminative appearance modeling alleviate the generalization problem to some extent
Ordinal regression methods: survey and experimental study
Abstract—Ordinal regression problems are those machine learning problems where the objective is to classify patterns using a
categorical scale which shows a natural order between the labels. Many real-world applications present this labelling structure and
that has increased the number of methods and algorithms developed over the last years in this field. Although ordinal regression can
be faced using standard nominal classification techniques, there are several algorithms which can specifically benefit from the ordering
information. Therefore, this paper is aimed at reviewing the state of the art on these techniques and proposing a taxonomy based on
how the models are constructed to take the order into account. Furthermore, a thorough experimental study is proposed to check if
the use of the order information improves the performance of the models obtained, considering some of the approaches within the
taxonomy. The results confirm that ordering information benefits ordinal models improving their accuracy and the closeness of the
predictions to actual targets in the ordinal scal
Quantized Radio Map Estimation Using Tensor and Deep Generative Models
Spectrum cartography (SC), also known as radio map estimation (RME), aims at
crafting multi-domain (e.g., frequency and space) radio power propagation maps
from limited sensor measurements. While early methods often lacked theoretical
support, recent works have demonstrated that radio maps can be provably
recovered using low-dimensional models -- such as the block-term tensor
decomposition (BTD) model and certain deep generative models (DGMs) -- of the
high-dimensional multi-domain radio signals. However, these existing provable
SC approaches assume that sensors send real-valued (full-resolution)
measurements to the fusion center, which is unrealistic. This work puts forth a
quantized SC framework that generalizes the BTD and DGM-based SC to scenarios
where heavily quantized sensor measurements are used. A maximum likelihood
estimation (MLE)-based SC framework under a Gaussian quantizer is proposed.
Recoverability of the radio map using the MLE criterion are characterized under
realistic conditions, e.g., imperfect radio map modeling and noisy
measurements. Simulations and real-data experiments are used to showcase the
effectiveness of the proposed approach.Comment: 16 pages, 9 figure
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