Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning

Zhang, Zhao; Jiang, Weizhong; Zheng, Zhang; Li, Sheng; Liu, Guangcan; Qin, Jie

doi:10.24963/ijcai.2019/608

Cited by 54 publications

(38 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…classification error is not incorporated in the objective function). In several variants of discriminative DL methods are proposed to improve the data representation and classification abilities by encoding the locality and reconstruction error into the DL procedures, while some of them aim to concurrently improve the scalability of the algorithms by getting rid of costly norms [26,27,28]. Recently, DL has also been extended to deep learning frameworks [29], which seek multiple dictionaries at different image scales capturing also complementary coherent characteristics.…”

Section: Related Workmentioning

confidence: 99%

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

et al. 2020

View full text Add to dashboard Cite

The field of neuroscience is experiencing rapid growth in the complexity and quantity of the recorded neural activity allowing us unprecedented access to its dynamics in different brain areas. The objective of this work is to discover directly from the experimental data rich and comprehensible models for brain function that will be concurrently robust to noise. Considering this task from the perspective of dimensionality reduction, we develop an innovative, robust to noise dictionary learning framework based on adversarial training methods for the identification of patterns of synchronous firing activity as well as within a time lag. We employ real-world binary datasets describing the spontaneous neuronal activity of laboratory mice over time, and we aim to their efficient low-dimensional representation. The results on the classification accuracy for the discrimination between the clean and the adversarial-noisy activation patterns obtained by an SVM classifier highlight the efficacy of the proposed scheme compared to other methods, and the visualization of the dictionary's distribution demonstrates the multifarious information that we obtain from it.

show abstract

Section: Related Workmentioning

confidence: 99%

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, CS theory states that if x j is sufficiently sparse in a transform (Ψ Ψ Ψ) domain, exact recovery is possible. Several recovery methods have been developed for this purpose [34], [35], [36], [37]. Specifically, if the transform coefficients, v j = Ψ Ψ Ψx j , are sufficiently sparse, the solution of the recovery procedure can be found with several l 0 optimization procedures or their l 1 -based convex relaxations that use pursuit-based methods [15], [16].…”

Section: Introductionmentioning

confidence: 99%

Soft Video Multicasting Using Adaptive Compressed Sensing

Hadizadeh

Bajić

2021

IEEE Trans. Multimedia

View full text Add to dashboard Cite

Recently, soft video multicasting has gained a lot of attention, especially in broadcast and mobile scenarios where the bit rate supported by the channel may differ across receivers, and may vary quickly over time. Unlike the conventional designs that force the source to use a single bit rate according to the receiver with the worst channel quality, soft video delivery schemes transmit the video such that the video quality at each receiver is commensurate with its specific instantaneous channel quality. In this paper, we present a soft video multicasting system using an adaptive block-based compressed sensing (BCS) method. The proposed system consists of an encoder, a transmission system, and a decoder. At the encoder side, each block in each frame of the input video is adaptively sampled with a rate that depends on the texture complexity and visual saliency of the block. The obtained BCS samples are then placed into several packets, and the packets are transmitted via a channel-aware OFDM (orthogonal frequency division multiplexing) transmission system with a number of subchannels. At the decoder side, the received BCS samples are first used to build an initial approximation of the transmitted frame. To further improve the reconstruction quality, an iterative BCS reconstruction algorithm is then proposed that uses an adaptive transform and an adaptive soft-thresholding operator, which exploits the temporal similarity between adjacent frames to achieve better reconstruction quality. The extensive objective and subjective experimental results indicate the superiority of the proposed system over the state-of-the-art soft video multicasting systems.

show abstract

“…The reason is that the pose, illumination and expression information can be implicitly encode into the dictionary. Many variants of dictionary learning methods have been proposed [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Structural Constraint and Discriminative Constraint Based Dictionary Pair Learning for Image Classification

Yang¹,

Wang

et al. 2020

IEEE Access

View full text Add to dashboard Cite

The dictionary learning algorithm is an effective image classification algorithm. To tackle with the problems including noise, occlusion, small sample problem, and so on, many discriminative dictionary learning models have been proposed. However, the performance of these algorithms might not be satisfactory, the main reason is that some useful features are not effectively disclosed. For example, the coding coefficients of the samples have row sparsity consistency and the block structure property. Maintaining the inherent features of the samples (e.g. the block structure, local characteristics of atoms) is beneficial to enhance the discriminative ability of the model. In addition, a powerful discriminative constraint is also essential to improve the performance of image classification. In the paper, we propose a new structural constraint and discriminative constraint based dictionary pair learning for image classification. In the model, the L21 norm constraint is applied on the analysis sub-dictionary to ensure the row sparsity consistency of the coding coefficients as much as possible. A new discriminative constraint is designed to enforce the representation matrix to be more discriminative, which can be acquired by minimizing the intra-class scatter and maximizing the inter-class scatter of the samples. Besides, we define a new atomic locality constraint, which forces the atoms to preserve the structure information of the samples. Finally, seven benchmark data sets are selected to evaluate the performance of the proposed method in comparison with popular methods. The experimental results outperform the state-of-the-art methods, which demonstrate the efficacy of the proposed model.

show abstract

Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning

Cited by 54 publications

References 9 publications

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

Adversarial dictionary learning for a robust analysis and modelling of spontaneous neuronal activity

Soft Video Multicasting Using Adaptive Compressed Sensing

Structural Constraint and Discriminative Constraint Based Dictionary Pair Learning for Image Classification

Contact Info

Product

Resources

About