6 papers
Multivariate signal restoration via Multifold Graph Learning
Haruki Yokota, Yuichi Tanaka
We propose a method for restoring noisy and incomplete multivariate signals by learning multifold graphs. Spatial relationships among channels can be utilized to recover missing va…
Multimodal Signal Restoration with Signed Twofold Graph Learning
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka
We propose a method for jointly learning signed twofold graphs and performing signal restoration on multimodal graph signals. Multimodal signals on sensor networks are commonly mod…
Auditory Attention Decoding without Spatial Information: A Diotic EEG Study
Masahiro Yoshino, Haruki Yokota, Junya Hara +2
Auditory attention decoding (AAD) identifies the attended speech stream in multi-speaker environments by decoding brain signals such as electroencephalography (EEG). This technolog…
Efficient Learning of Balanced Signed Graphs via Sparse Linear Programming
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka +1
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph is a signed gr…
Efficient Learning of Balanced Signed Graphs via Iterative Linear Programming
Haruki Yokota, Hiroshi Higashi, Yuichi Tanaka +1
Signed graphs are equipped with both positive and negative edge weights, encoding pairwise correlations as well as anti-correlations in data. A balanced signed graph has no cycles…
Time-Varying Graph Learning with Constraints on Graph Temporal Variation
Haruki Yokota, Koki Yamada, Yuichi Tanaka +1
We propose a novel framework for learning time-varying graphs from spatiotemporal measurements. Given an appropriate prior on the temporal behavior of signals, our proposed method…