57 research outputs found
Frequency Recognition in SSVEP-based BCI using Multiset Canonical Correlation Analysis
Canonical correlation analysis (CCA) has been one of the most popular methods
for frequency recognition in steady-state visual evoked potential (SSVEP)-based
brain-computer interfaces (BCIs). Despite its efficiency, a potential problem
is that using pre-constructed sine-cosine waves as the required reference
signals in the CCA method often does not result in the optimal recognition
accuracy due to their lack of features from the real EEG data. To address this
problem, this study proposes a novel method based on multiset canonical
correlation analysis (MsetCCA) to optimize the reference signals used in the
CCA method for SSVEP frequency recognition. The MsetCCA method learns multiple
linear transforms that implement joint spatial filtering to maximize the
overall correlation among canonical variates, and hence extracts SSVEP common
features from multiple sets of EEG data recorded at the same stimulus
frequency. The optimized reference signals are formed by combination of the
common features and completely based on training data. Experimental study with
EEG data from ten healthy subjects demonstrates that the MsetCCA method
improves the recognition accuracy of SSVEP frequency in comparison with the CCA
method and other two competing methods (multiway CCA (MwayCCA) and phase
constrained CCA (PCCA)), especially for a small number of channels and a short
time window length. The superiority indicates that the proposed MsetCCA method
is a new promising candidate for frequency recognition in SSVEP-based BCIs
Bayesian Robust Tensor Factorization for Incomplete Multiway Data
We propose a generative model for robust tensor factorization in the presence
of both missing data and outliers. The objective is to explicitly infer the
underlying low-CP-rank tensor capturing the global information and a sparse
tensor capturing the local information (also considered as outliers), thus
providing the robust predictive distribution over missing entries. The
low-CP-rank tensor is modeled by multilinear interactions between multiple
latent factors on which the column sparsity is enforced by a hierarchical
prior, while the sparse tensor is modeled by a hierarchical view of Student-
distribution that associates an individual hyperparameter with each element
independently. For model learning, we develop an efficient closed-form
variational inference under a fully Bayesian treatment, which can effectively
prevent the overfitting problem and scales linearly with data size. In contrast
to existing related works, our method can perform model selection automatically
and implicitly without need of tuning parameters. More specifically, it can
discover the groundtruth of CP rank and automatically adapt the sparsity
inducing priors to various types of outliers. In addition, the tradeoff between
the low-rank approximation and the sparse representation can be optimized in
the sense of maximum model evidence. The extensive experiments and comparisons
with many state-of-the-art algorithms on both synthetic and real-world datasets
demonstrate the superiorities of our method from several perspectives.Comment: in IEEE Transactions on Neural Networks and Learning Systems, 201
An adaptive weighted self-representation method for incomplete multi-view clustering
For multi-view data in reality, part of its elements may be missing because
of human or machine error. Incomplete multi-view clustering (IMC) clusters the
incomplete multi-view data according to the characters of various views of the
instances. Recently, IMC has attracted much attention and many related methods
have been proposed. However, the existing approaches still need to be developed
and innovated in the following aspects: (1) Current methods only consider the
differences of different views, while the different influences of instances, as
well as distinguishes between missing values and completed values are ignored.
(2) The updating scheme for weighting matrix in adaptive weighted algorithms
usually relies on an optimization sub-problem, whose optimal solution may not
be easy to achieve. (3) The adaptive weighted subspace algorithms that can
recover the incomplete data are anchor types. The randomness of the anchor
matrix may cause unreliability. To tackle these limitations, we propose an
adaptive weighted self-representation (AWSR) subspace method for IMC. The AWSR
method tunes the weighting matrix adaptively in accordance with the views of
different instances and the recovery process of the missing values. The low
rank and smoothness constraints on the representation matrix make the subspace
reveal the underlying features of the dataset accurately. We also analyze the
convergence property of the block coordinate method for our optimization model
theoretically. Numerical performance on five real-world data shows that the
AWSR method is effective and delivers superior results when compared to other
eight widely-used approaches considering the clustering accuracy (ACC),
normalized mutual information (NMI) and Purity
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