Simple Poisson PCA: an algorithm for (sparse) feature extraction with simultaneous dimension determination

Smallman, Luke; Underwood, William; Artemiou, Andreas

doi:10.1007/s00180-019-00903-0

Cited by 8 publications

(6 citation statements)

References 13 publications

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“…The adaptive L 0 procedure applied to SVR in this paper has been applied to linear regression by Frommlet and Nuel (2016) [7] and variations of this idea have been implemented in the SVM literature, see, for example, Li et al (2015) [10] and Huang et al (2008) [15]. It has also been used by Smallman et al (2020) [16] for sparse feature extraction in principal component analysis (PCA) for Poisson distributed data, which is useful in dimension reduction for text data. It is a very appealing approach as it is computationally efficient (although it needs to be applied iteratively) to obtain an accurate solution and it is also efficient in providing sparse solutions.…”

Section: Discussionmentioning

confidence: 99%

Adaptive L0 Regularization for Sparse Support Vector Regression

Christou

Artemiou

2023

Mathematics

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In this work, we proposed a sparse version of the Support Vector Regression (SVR) algorithm that uses regularization to achieve sparsity in function estimation. To achieve this, we used an adaptive L0 penalty that has a ridge structure and, therefore, does not introduce additional computational complexity to the algorithm. In addition to this, we used an alternative approach based on a similar proposal in the Support Vector Machine (SVM) literature. Through numerical studies, we demonstrated the effectiveness of our proposals. We believe that this is the first time someone discussed a sparse version of Support Vector Regression (in terms of variable selection and not in terms of support vector selection).

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Section: Discussionmentioning

confidence: 99%

Adaptive L0 Regularization for Sparse Support Vector Regression

Christou

Artemiou

2023

Mathematics

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“…In [46] putthe authors used an adaptive L0 penalty due to [16] on the simple exponential PCA algorithm. They place the penalty on W , the analogue of the loadings matrix.…”

Section: Sparse Simple Poisson Pcamentioning

confidence: 99%

A Literature Review of (Sparse) Exponential Family PCA

Smallman

Artemiou

2022

J Stat Theory Pract

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“…As emotion recognition uses different data types, the feature extraction methods for different types of data will also be different. Commonly used feature extraction methods are CNN (Brousseau et al, 2020;Cabada et al, 2020), LSTM (Wu et al, 2019;Zou et al, 2020), PCA (Smallman et al, 2020), etc. The third is the difference in classification models.…”

Section: Introductionmentioning

confidence: 99%

A Novel User Emotional Interaction Design Model Using Long and Short-Term Memory Networks and Deep Learning

Chen

Huang

et al. 2021

Front. Psychol.

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Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient (MFCC) of the speech signal is used as the input of the improved long and short-term memory network (ILSTM). To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed.

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Simple Poisson PCA: an algorithm for (sparse) feature extraction with simultaneous dimension determination

Cited by 8 publications

References 13 publications

Adaptive L0 Regularization for Sparse Support Vector Regression

Adaptive L0 Regularization for Sparse Support Vector Regression

A Literature Review of (Sparse) Exponential Family PCA

A Novel User Emotional Interaction Design Model Using Long and Short-Term Memory Networks and Deep Learning

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