In this work, a kernel principle component analysis network (KPCANet) is proposed for classification of the facial expression in unconstrained images, which comprises only the very basic data processing components: ca...
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Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. An improved representation called Local Differenc...
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pattern matching is a fundamental application in biomedicine and biological sequence analysis. A wildcard can match any one character in a sequence. Multiple wildcards form a gap. A flexible wildcard gap can match any...
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This paper presents a deep learning method application to the extraction of emotions included in Chinese speech with a deep belief network (DBN) structure. Eight proper features such as pitch, mel frequency cepstrum c...
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ISBN:
(纸本)9781479974351
This paper presents a deep learning method application to the extraction of emotions included in Chinese speech with a deep belief network (DBN) structure. Eight proper features such as pitch, mel frequency cepstrum coefficient (MFCC) are chosen from Mandarin speech used as network inputs, and a DBN classifier is used instead of traditional shallow learning methods to recognition of emotions. Experiment studies have proven that its recognition rate is higher than that of the traditional back propagation (BP) method and support vector machine (SVM) classifier.
In view of the multi-view face detection problem under complex background, an improved face detection method based on multi-features boosting collaborative learning algorithm integrating local binary pattern (LBP) is ...
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In order to verify the network traffic decline because by node breakdown, this paper proposes a new type of prediction algorithm (Prediction algorithm based on Discrete-Queue for FARIMA model, PDF). At first, the math...
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Center nodes have a bigger load and burden with lots of routing in an Ad Hoc Network Model. Congestion of the nodes' packets has a great impact on network performance, especially in wireless networks. This paper p...
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Feature subset selection is an important approach to deal with high-dimensional data. But selecting the best subset of data is NP hard. So most of feature selection methods cannot handle high-dimensional data efficien...
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Feature subset selection is an important approach to deal with high-dimensional data. But selecting the best subset of data is NP hard. So most of feature selection methods cannot handle high-dimensional data efficiently, or they can only obtain local optimum instead of global optimum. In these cases, when the data consist of both labeled and unlabeled data, semi-supervised feature selection can make full use of data information. In this paper, we introduce a novel semi-supervised feature selection algorithm, which is a filter method based on Fisher-Markov selector, thus ours can achieve global optimum and computational efficiency under certain kernels.
In this paper, we present a new speech enhancement method based on robust principal component analysis. In the proposed method, noisy signal is transformed into time-frequency domain where background noise is assumed ...
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A deep Neural Network model was trained to classify the facial expression in unconstrained images, which comprises nine layers, including input layer, convolutional layer, pooling layer, fully connected layers and out...
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