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International Joint Conference on Neural Networks (IJCNN)

作者： Thom, Markus Schweiger, Roland Palm, Guenther Daimler AG Dept Environm Percept GR PAP Ulm Germany

ISBN: (纸本)9781424496365

Non-negative matrix factorization is a technique for decomposing large data sets into bases and code words, where all entries of the occurring matrices are non-negative. A recently proposed technique also incorporates sparseness constraints, in such a way that the amount of nonzero entries in both bases and code words becomes controllable. This paper extends the Non-negative matrix factorization with Sparseness Constraints. First, a modification of the optimization criteria ensures fast inference of the code words. Thus, the approach is real-time capable for use in time critical applications. Second, in case a teacher signal is associated with the samples, it is considered in order to ensure that inferred code words of different classes can be well distinguished. Thus, the derived bases generate discriminative code words, which is a crucial prerequisite for training powerful classifiers. Experiments on natural image patches show, similar to recent results in the field of sparse coding algorithms, that Gabor-like filters are minimizing the reconstruction error while retaining inference capabilities. However, applying the approach with incorporation of the teacher signal to handwritten digits yields morphologically completely different bases, while achieving superior classification results.

关键词： Computer architecture Encoding Gabor-like filters matrix decomposition Optimization Sparse matrices Training Transfer functions discriminative code words fast inference handwritten digits inference capabilities large data set decomposition matrix algebra natural image patches nonnegative matrix factorization nonzero entries optimisation optimization criteria sparse coding algorithms sparseness constraints supervised matrix factorization teacher signal time critical applications very large databases

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Informed Multimodal Latent Subspace Learning via supervised matrix factorization 16

Informed Multimodal Latent Subspace Learning via Supervised ...

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10th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP)

作者： Gaurav, Ramashish Verma, Mridula Shukla, K. K. BHU Indian Inst Technol Varanasi Uttar Pradesh India

ISBN: (纸本)9781450347532

matrix factorization technique has been widely used as a popular method to learn a joint latent-compact subspace, when multiple views or modals of objects (belonging to single-domain or multiple-domain) are available. Our work confronts the problem of learning an informative latent subspace by imparting supervision to matrix factorization for fusing multiple modals of objects, where we devise simpler supervised additive updates instead of multiplicative updates, thus scalable to large scale datasets. To increase the classification accuracy we integrate the label information of images with the process of learning a semantically enhanced subspace. We perform extensive experiments on two publicly available standard image datasets of NUS WIDE and compare the results with state-of-the-art subspace learning and fusion techniques to evaluate the efficacy of our framework. Improvement obtained in the classification accuracy confirms the effectiveness of our approach. In essence, we propose a novel method for supervised data fusion thus leading to supervised subspace learning.

关键词： Image Classification Joint Subspace Learning supervised matrix factorization

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Large-scale supervised similarity learning in networks

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KNOWLEDGE AND INFORMATION SYSTEMS 2016年第3期48卷 707-740页

作者： Chang, Shiyu Qi, Guo-Jun Yang, Yingzhen Aggarwal, Charu C. Zhou, Jiayu Wang, Meng Huang, Thomas S. Univ Illinois Beckman Inst Urbana IL 61801 USA Univ Cent Florida Orlando FL 32816 USA IBM TJ Watson Res Ctr Yorktown Hts NY 10598 USA Michigan State Univ E Lansing MI 48824 USA Hefei Univ Technol Hefei 230009 Anhui Peoples R China

The problem of similarity learning is relevant to many data mining applications, such as recommender systems, classification, and retrieval. This problem is particularly challenging in the context of networks, which contain different aspects such as the topological structure, content, and user supervision. These different aspects need to be combined effectively, in order to create a holistic similarity function. In particular, while most similarity learning methods in networks such as SimRank utilize the topological structure, the user supervision and content are rarely considered. In this paper, a factorized similarity learning (FSL) is proposed to integrate the link, node content, and user supervision into a uniform framework. This is learned by using matrix factorization, and the final similarities are approximated by the span of low-rank matrices. The proposed framework is further extended to a noise-tolerant version by adopting a hinge loss alternatively. To facilitate efficient computation on large-scale data, a parallel extension is developed. Experiments are conducted on the DBLP and CoRA data sets. The results show that FSL is robust and efficient and outperforms the state of the art. The code for the learning algorithm used in our experiments is available at http://***/similar to chang87/.

关键词： supervised network similarity learning supervised network embedding Large-scale network supervised matrix factorization Link content consistency

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Collaborative multimodal feature learning for RGB-D action recognition

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JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 2019年 59卷 537-549页

作者： Kong, Jun Liu, Tianshan Jiang, Min Jiangnan Univ Jiangsu Prov Engn Lab Pattern Recognit & Computat Wuxi 214122 Peoples R China

The emergence of cost-effective depth sensors opens up a new dimension for RGB-D based human action recognition. In this paper, we propose a collaborative multimodal feature learning (CMFL) model for human action recognition from RGB-D sequences. Specifically, we propose a robust spatio-temporal pyramid feature (RSTPF) to capture dynamic local patterns around each human joint. The proposed CMFL model fuses multimodal data (skeleton, depth and RGB), and learns action classifiers using the fused features. The original low-level feature matrices are factorized to learn shared features and modality-specific features under a supervised fashion. The shared features describe the common structures among the three modalities while the modality-specific features capture intrinsic information of each modality. We formulate shared-specific features mining and action classifiers learning in a unified max-margin framework, and solve the formulation using an iterative optimization algorithm. Experimental results on four action datasets demonstrate the efficacy of the proposed method. (C) 2019 Elsevier Inc. All rights reserved.

关键词： RGB-D action recognition Multimodal data Max-margin learning framework supervised matrix factorization

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Discriminative Relational Representation Learning for RGB-D Action Recognition

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2016年第6期25卷 2856-2865页

作者： Kong, Yu Fu, Yun Northeastern Univ Dept Elect & Comp Engn Boston MA 02115 USA Northeastern Univ Coll Comp & Informat Sci Dept Elect & Comp Engn Boston MA 02115 USA

This paper addresses the problem of recognizing human actions from RGB-D videos. A discriminative relational feature learning method is proposed for fusing heterogeneous RGB and depth modalities, and classifying the actions in RGB-D sequences. Our method factorizes the feature matrix of each modality, and enforces the same semantics for them in order to learn shared features from multimodal data. This allows us to capture the complex correlations between the two modalities. To improve the discriminative power of the relational features, we introduce a hinge loss to measure the classification accuracy when the features are employed for classification. This essentially performs supervised factorization, and learns discriminative features that are optimized for classification. We formulate the recognition task within a maximum margin framework, and solve the formulation using a coordinate descent algorithm. The proposed method is extensively evaluated on two public RGB-D action data sets. We demonstrate that the proposed method can learn extremely low-dimensional features with superior discriminative power, and outperforms the state-of-the-art methods. It also achieves high performance when one modality is missing in testing or training.

关键词： Action recognition RGB-D camera heterogeneous data supervised matrix factorization

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14th IEEE International Conference on Data Mining (IEEE ICDM)

作者： Chang, Shiyu Qi, Guo-Jun Aggarwal, Charu C. Zhou, Jiayu Wang, Meng Huang, Thomas S. Univ Illinois Beckman Inst Urbana IL 61801 USA Univ Cent Florida Orlando FL 32816 USA IBM Corp TJ Watson Res Ctr Yorktown Hts NY 10598 USA Arizona State Univ Tempe AZ 85281 USA Hefei Univ Technol Hefei 230009 Anhui Peoples R China

ISBN: (纸本)9781479943036

The problem of similarity learning is relevant to many data mining applications, such as recommender systems, classification, and retrieval. This problem is particularly challenging in the context of networks, which contain different aspects such as the topological structure, content, and user supervision. These different aspects need to be combined effectively, in order to create a holistic similarity function. In particular, while most similarity learning methods in networks such as SimRank utilize the topological structure, the user supervision and content are rarely considered. In this paper, a Factorized Similarity Learning (FSL) is proposed to integrate the link, node content, and user supervision into an uniform framework. This is learned by using matrix factorization, and the final similarities are approximated by the span of low rank matrices. The proposed framework is further extended to a noise-tolerant version by adopting a hinge-loss alternatively. To facilitate efficient computation on large scale data, a parallel extension is developed. Experiments are conducted on the DBLP and CoRA datasets. The results show that FSL is robust, efficient, and outperforms the state-of-theart.

关键词： Content Link Network similarity supervised matrix factorization Supervision

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