2,002 research outputs found
Spectra of some invertible weighted composition operators on Hardy and weighted Bergman spaces in the unit ball
In this paper, we investigate the spectra of invertible weighted composition
operators with automorphism symbols, on Hardy space and
weighted Bergman spaces , where is the
unit ball of the -dimensional complex space. By taking ,
the unit disc, we also complete the discussion about
the spectrum of a weighted composition operator when it is invertible on
or .Comment: 23 Page
Numerical Ranges of Composition Operators with Elliptic Automorphism Symbols
In this paper we investigate the numerical ranges of composition operators
whose symbols are elliptic automorphisms of finite orders, on the Hilbert Hardy
space .Comment: 14 Page
Layer-refined Graph Convolutional Networks for Recommendation
Recommendation models utilizing Graph Convolutional Networks (GCNs) have
achieved state-of-the-art performance, as they can integrate both the node
information and the topological structure of the user-item interaction graph.
However, these GCN-based recommendation models not only suffer from
over-smoothing when stacking too many layers but also bear performance
degeneration resulting from the existence of noise in user-item interactions.
In this paper, we first identify a recommendation dilemma of over-smoothing and
solution collapsing in current GCN-based models. Specifically, these models
usually aggregate all layer embeddings for node updating and achieve their best
recommendation performance within a few layers because of over-smoothing.
Conversely, if we place learnable weights on layer embeddings for node
updating, the weight space will always collapse to a fixed point, at which the
weighting of the ego layer almost holds all. We propose a layer-refined GCN
model, dubbed LayerGCN, that refines layer representations during information
propagation and node updating of GCN. Moreover, previous GCN-based
recommendation models aggregate all incoming information from neighbors without
distinguishing the noise nodes, which deteriorates the recommendation
performance. Our model further prunes the edges of the user-item interaction
graph following a degree-sensitive probability instead of the uniform
distribution. Experimental results show that the proposed model outperforms the
state-of-the-art models significantly on four public datasets with fast
training convergence. The implementation code of the proposed method is
available at https://github.com/enoche/ImRec.Comment: 12 pages, 5 figure
Huber Principal Component Analysis for Large-dimensional Factor Models
Factor models have been widely used in economics and finance. However, the
heavy-tailed nature of macroeconomic and financial data is often neglected in
the existing literature. To address this issue and achieve robustness, we
propose an approach to estimate factor loadings and scores by minimizing the
Huber loss function, which is motivated by the equivalence of conventional
Principal Component Analysis (PCA) and the constrained least squares method in
the factor model. We provide two algorithms that use different penalty forms.
The first algorithm, which we refer to as Huber PCA, minimizes the
-norm-type Huber loss and performs PCA on the weighted sample
covariance matrix. The second algorithm involves an element-wise type Huber
loss minimization, which can be solved by an iterative Huber regression
algorithm. Our study examines the theoretical minimizer of the element-wise
Huber loss function and demonstrates that it has the same convergence rate as
conventional PCA when the idiosyncratic errors have bounded second moments. We
also derive their asymptotic distributions under mild conditions. Moreover, we
suggest a consistent model selection criterion that relies on rank minimization
to estimate the number of factors robustly. We showcase the benefits of Huber
PCA through extensive numerical experiments and a real financial portfolio
selection example. An R package named ``HDRFA" has been developed to implement
the proposed robust factor analysis
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