90 research outputs found
Model selection of polynomial kernel regression
Polynomial kernel regression is one of the standard and state-of-the-art
learning strategies. However, as is well known, the choices of the degree of
polynomial kernel and the regularization parameter are still open in the realm
of model selection. The first aim of this paper is to develop a strategy to
select these parameters. On one hand, based on the worst-case learning rate
analysis, we show that the regularization term in polynomial kernel regression
is not necessary. In other words, the regularization parameter can decrease
arbitrarily fast when the degree of the polynomial kernel is suitable tuned. On
the other hand,taking account of the implementation of the algorithm, the
regularization term is required. Summarily, the effect of the regularization
term in polynomial kernel regression is only to circumvent the " ill-condition"
of the kernel matrix. Based on this, the second purpose of this paper is to
propose a new model selection strategy, and then design an efficient learning
algorithm. Both theoretical and experimental analysis show that the new
strategy outperforms the previous one. Theoretically, we prove that the new
learning strategy is almost optimal if the regression function is smooth.
Experimentally, it is shown that the new strategy can significantly reduce the
computational burden without loss of generalization capability.Comment: 29 pages, 4 figure
Improved Ensemble Empirical Mode Decomposition and its Applications to Gearbox Fault Signal Processing
Abstract Ensemble empirical mode decomposition (EEMD) is a noiseassisted method and also a significant improvement on empirical mode decomposition (EMD). However, the EEMD method lacks a guide to choosing the appropriate amplitude of added noise and its computation efficiency is fairly low. To alleviate the problems of the EEMD method, the improved complementary EEMD method (ICEEMD) was proposed. Furthermore, the ICEEMD method was used to analyze realistic gearbox faulty signals. The results indicate that the ICEEMD method has some advantages over the EEMD method in alleviating the mode mixing and splitting as well as reducing the time cost and also outperforms the CEEMD method in alleviating the mode mixing and splitting. The paper also indicates that the ICEEMD method seems to be an effective and efficient method for processing gearbox fault signals
LSTM Pose Machines
We observed that recent state-of-the-art results on single image human pose
estimation were achieved by multi-stage Convolution Neural Networks (CNN).
Notwithstanding the superior performance on static images, the application of
these models on videos is not only computationally intensive, it also suffers
from performance degeneration and flicking. Such suboptimal results are mainly
attributed to the inability of imposing sequential geometric consistency,
handling severe image quality degradation (e.g. motion blur and occlusion) as
well as the inability of capturing the temporal correlation among video frames.
In this paper, we proposed a novel recurrent network to tackle these problems.
We showed that if we were to impose the weight sharing scheme to the
multi-stage CNN, it could be re-written as a Recurrent Neural Network (RNN).
This property decouples the relationship among multiple network stages and
results in significantly faster speed in invoking the network for videos. It
also enables the adoption of Long Short-Term Memory (LSTM) units between video
frames. We found such memory augmented RNN is very effective in imposing
geometric consistency among frames. It also well handles input quality
degradation in videos while successfully stabilizes the sequential outputs. The
experiments showed that our approach significantly outperformed current
state-of-the-art methods on two large-scale video pose estimation benchmarks.
We also explored the memory cells inside the LSTM and provided insights on why
such mechanism would benefit the prediction for video-based pose estimations.Comment: Poster in IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 201
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