Search · four archives
Search · four archives
21 papers · ranked by Valyu relevance
Zhisong Wang, Alexander Maier, Nikos K. Logothetis, Hualou Liang
The study of the neuronal correlates of the spontaneous alternation in perception elicited by bistable visual stimuli is promising for understanding the mechanism of neural information processing and the neural basis of visual perception and perceptual decision-making. In this paper, we develop a sparse nonnegative…
Yangyang Xu
Multi-way data arises in many applications such as electroencephal-ography (EEG) classification, face recognition, text mining and hyperspectral data analysis. Tensor decomposition has been commonly used to find the hidden factors and elicit the intrinsic structures of the multi-way data. This paper considers sparse…
Xiongjun Zhang, Michael K. Ng
Tensor decomposition is a powerful tool for extracting physically meaningful latent factors from multi-dimensional nonnegative data, and has been an increasing interest in a variety of fields such as image processing, machine learning, and computer vision. In this paper, we propose a sparse nonnegative Tucker…
A. Cichocki, M. Mørup, P. Smaragdis, W. Wang + 1 more
Nonnegative matrix factorization (NMF) and its extension known as nonnegative tensor factorization (NTF) are emerging techniques that have been proposed recently. The goal of NMF/NTF is to decompose a nonnegative data matrix into a product of lower-rank nonnegative matrices or tensors (i.e., multiway arrays). An NMF…
Yunhui Fu, Shin Matsushima, Kenji Yamanishi
Non-negative tensor factorization (NTF) is a widely used multi-way analysis approach that factorizes a high-order non-negative data tensor into several non-negative factor matrices. In NTF, the non-negative rank has to be predetermined to specify the model and it greatly influences the factorized matrices. However, its…
Min Shi, Liubou Klindziuk, Shamim Mollah
An in-depth understanding of epithelial breast cell responses to the growth-promoting ligands is required to elucidate how the microenvironment (ME) signals affect cell-intrinsic regulatory networks and the cellular phenotypes they control, such as cell growth, progression, and differentiation. This is particularly…
Chong Peng, Yiqun Zhang, Yongyong Chen, Kang Zhao + 2 more
'Qiang Cheng'] Nonnegative matrix factorization (NMF) has been widely studied in recent years due to its effectiveness in representing nonnegative data with parts-based representations. For NMF, a sparser solution implies better parts-based representation. However, current NMF methods do not always generate sparse…
Ko Abe, Shintaro Yuki, Teppei Shimamura
Combinatorial indexing-based single-cell RNA sequencing methods such as sci-RNA-seq and sci-RNA-seq3 now enable the profiling of millions of cells, producing expression matrices that are both extremely sparse and high-dimensional. Conventional nonnegative matrix factorization (NMF) provides an interpretable framework…
Vamsi K. Potluru, Sergey M. Plis, Jonathan Le Roux, Barak A. Pearlmutter + 2 more
'Barak A. Pearlmutter' 'Vince D. Calhoun' 'Thomas P. Hayes'] Nonnegative matrix factorization (NMF) has become a ubiquitous tool for data analysis. An important variant is the sparse NMF problem which arises when we explicitly require the learnt features to be sparse. A natural measure of sparsity is the L0 norm…
Dongjin Choi, Jun-Gi Jang, U Kang, Junwen Wang
How can we extract hidden relations from a tensor and a matrix data simultaneously in a fast, accurate, and scalable way? Coupled matrix-tensor factorization (CMTF) is an important tool for this purpose. Designing an accurate and efficient CMTF method has become more crucial as the size and dimension of real-world data…
Madhusudana Shashanka, Bhiksha Raj, Paris Smaragdis
This paper presents a family of probabilistic latent variable models that can be used for analysis of nonnegative data. We show that there are strong ties between nonnegative matrix factorization and this family, and provide some straightforward extensions which can help in dealing with shift invariances, higher-order…
Qingshui Liao, Qilong Liu, Fatimah Abdul Razak
Tucker decomposition is widely used for image representation, data reconstruction, and machine learning tasks, but the calculation cost for updating the Tucker core is high. Bilevel form of triple decomposition (TriD) overcomes this issue by decomposing the Tucker core into three low-dimensional third-order factor…
Amy N. Langville, Carl D. Meyer, Russell Albright, James A. Cox + 1 more
'David Duling'] It is well-known that good initializations can improve the speed and accuracy of the solutions of many nonnegative matrix factorization (NMF) algorithms [56]. Many NMF algorithms are sensitive with respect to the initialization of W or H or both. This is especially true of algorithms of the alternating…
Riyasat Ohib, Nicolas Gillis, Niccolò Dalmasso, Sameena Shah + 2 more
'Vamsi K. Potluru' 'Sergey M. Plis'] As evident from deep learning, very large models bring improvements in training dynamics and representation power. Yet, smaller models have benefits of energy efficiency and interpretability. To get the benefits from both ends of the spectrum we often encourage sparsity in the…
Xihui Lin, Paul C. Boutros
Nonnegative matrix factorization (NMF) is a technique widely used in various fields, including artificial intelligence (AI), signal processing and bioinformatics. However existing algorithms and R packages cannot be applied to large matrices due to their slow convergence, and cannot handle missing values. In addition…
Sunho Park, Nabhonil Kar, Jae-Ho Cheong, Tae Hyun Hwang
Accurate identification of pathways associated with cancer phenotypes (e.g., cancer sub-types and treatment outcome) could lead to discovering reliable prognostic and/or predictive biomarkers for better patients stratification and treatment guidance. In our previous work, we have shown that non-negative matrix…
Wenya. Liu, Xiulin. Wang, Jing. Xu, Yi. Chang + 2 more
Previous researches demonstrate that major depression disorder (MDD) is associated with widespread network dysconnectivity, and the dynamics of functional connectivity networks are important to delineate the neural mechanisms of MDD. Cortical electroencephalography (EEG) oscillations act as coordinators to connect…
Michael Beyeler, Emily Rounds, Kristofor D. Carlson, Nikil Dutt + 1 more
Supported by recent computational studies, sparse coding and dimensionality reduction are emerging as a ubiquitous coding strategy across brain regions and modalities, allowing neurons to achieve nonnegative sparse coding (NSC) by efficiently encoding high-dimensional stimulus spaces using a sparse and parts-based…
Authors not listed
Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
Sanjar Adilov
Machine learning models for molecular-property prediction typically work with molecular representations in the form of fingerprints, descriptors, or graphs. In case of fingerprints and descriptors, molecular representations usually comprise thousands of features, which causes the curse of dimensionality for many…
Diba Behnoudfar, Cory Simon, Joshua Schrier
Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas…