Name
Title | ||
|---|---|---|
Nonnegative Tensor Train Decompositions for Multi-domain Feature Extraction and Clustering. |
Abstract | ||
|---|---|---|
Tensor train (TT) is one of the modern tensor decomposition models for low-rank approximation of high-order tensors. For nonnegative multiway array data analysis, we propose a nonnegative TT (NTT) decomposition algorithm for the NTT model and a hybrid model called the NTT-Tucker model. By employing the hierarchical alternating least squares approach, each fiber vector of core tensors is optimized efficiently at each iteration. We compared the performances of the proposed method with a standard nonnegative Tucker decomposition (NTD) algorithm by using benchmark data sets including event-related potential data and facial image data in multi-domain feature extraction and clustering tasks. It is illustrated that the proposed algorithm extracts physically meaningful features with relatively low storage and computational costs compared to the standard NTD model. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1007/978-3-319-46675-0_10 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
EEG,Feature extraction,HALS,Tucker decomposition | Data set,Pattern recognition,Tensor,Computer science,Feature extraction,Multi domain,Tucker decomposition,Artificial intelligence,Tensor train,Alternating least squares,Cluster analysis,Machine learning | Conference |
Volume | ISSN | Citations |
9949 | 0302-9743 | 2 |
PageRank | References | Authors |
0.38 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Namgil Lee | 1 | 56 | 6.09 |
| Anh Huy Phan | 2 | 828 | 51.60 |
| Fengyu Cong | 3 | 151 | 24.72 |
| Andrzej Cichocki | 4 | 2 | 1.06 |