23 citations · 96 across the 34 of their papers we have counts for
9 papers · 1 filter
Improving Nonparametric Classification via Local Radial Regression with an Application to Stock Prediction
Ruixing Cao, Akifumi Okuno, Kei Nakagawa +1
For supervised classification problems, this paper considers estimating the query's label probability through local regression using observed covariates. Well-known nonparametric k…
Extrapolation Towards Imaginary -Nearest Neighbour and Its Improved Convergence Rate
Akifumi Okuno, Hidetoshi Shimodaira
-nearest neighbour (-NN) is one of the simplest and most widely-used methods for supervised classification, that predicts a query's label by taking weighted ratio of observed…
Robust Graph Embedding with Noisy Link Weights
Akifumi Okuno, Hidetoshi Shimodaira
We propose -graph embedding for robustly learning feature vectors from data vectors and noisy link weights. A newly introduced empirical moment -score reduces the influence o…
Graph Embedding with Shifted Inner Product Similarity and Its Improved Approximation Capability
Akifumi Okuno, Geewook Kim, Hidetoshi Shimodaira
We propose shifted inner-product similarity (SIPS), which is a novel yet very simple extension of the ordinary inner-product similarity (IPS) for neural-network based graph embeddi…
On representation power of neural network-based graph embedding and beyond
Akifumi Okuno, Hidetoshi Shimodaira
We consider the representation power of siamese-style similarity functions used in neural network-based graph embedding. The inner product similarity (IPS) with feature vectors com…
A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks
Akifumi Okuno, Tetsuya Hada, Hidetoshi Shimodaira
A simple framework Probabilistic Multi-view Graph Embedding (PMvGE) is proposed for multi-view feature learning with many-to-many associations so that it generalizes various existi…
Cross-validation of matching correlation analysis by resampling matching weights
Hidetoshi Shimodaira
The strength of association between a pair of data vectors is represented by a nonnegative real number, called matching weight. For dimensionality reduction, we consider a linear t…
A simple coding for cross-domain matching with dimension reduction via spectral graph embedding
Hidetoshi Shimodaira
Data vectors are obtained from multiple domains. They are feature vectors of images or vector representations of words. Domains may have different numbers of data vectors with diff…
Computing p-values of LiNGAM outputs via Multiscale Bootstrap
Yusuke Komatsu, Shohei Shimizu, Hidetoshi Shimodaira
Structural equation models and Bayesian networks have been widely used to study causal relationships between continuous variables. Recently, a non-Gaussian method called LiNGAM was…