39 citations · 86 across the 13 of their papers we have counts for
14 papers
Identifiability of a statistical model with two latent vectors: Importance of the dimensionality relation and application to graph embedding
Hiroaki Sasaki
Identifiability of statistical models is a key notion in unsupervised representation learning. Recent work of nonlinear independent component analysis (ICA) employs auxiliary data…
Representation learning for maximization of MI, nonlinear ICA and nonlinear subspaces with robust density ratio estimation
Hiroaki Sasaki, Takashi Takenouchi
Contrastive learning is a recent promising approach in unsupervised representation learning where a feature representation of data is learned by solving a pseudo classification pro…
Robust contrastive learning and nonlinear ICA in the presence of outliers
Hiroaki Sasaki, Takashi Takenouchi, Ricardo Monti +1
Nonlinear independent component analysis (ICA) is a general framework for unsupervised representation learning, and aimed at recovering the latent variables in data. Recent practic…
Robust modal regression with direct log-density derivative estimation
Hiroaki Sasaki, Tomoya Sakai, Takafumi Kanamori
Modal regression is aimed at estimating the global mode (i.e., global maximum) of the conditional density function of the output variable given input variables, and has led to regr…
Neural-Kernelized Conditional Density Estimation
Hiroaki Sasaki, Aapo Hyvärinen
Conditional density estimation is a general framework for solving various problems in machine learning. Among existing methods, non-parametric and/or kernel-based methods are often…
Nonlinear ICA Using Auxiliary Variables and Generalized Contrastive Learning
Aapo Hyvarinen, Hiroaki Sasaki, Richard E. Turner
Nonlinear ICA is a fundamental problem for unsupervised representation learning, emphasizing the capacity to recover the underlying latent variables generating the data (i.e., iden…
Mode-Seeking Clustering and Density Ridge Estimation via Direct Estimation of Density-Derivative-Ratios
Hiroaki Sasaki, Takafumi Kanamori, Aapo Hyvärinen +2
Modes and ridges of the probability density function behind observed data are useful geometric features. Mode-seeking clustering assigns cluster labels by associating data samples…
Whitening-Free Least-Squares Non-Gaussian Component Analysis
Hiroaki Shiino, Hiroaki Sasaki, Gang Niu +1
Non-Gaussian component analysis (NGCA) is an unsupervised linear dimension reduction method that extracts low-dimensional non-Gaussian "signals" from high-dimensional data contamin…
Non-Gaussian Component Analysis with Log-Density Gradient Estimation
Hiroaki Sasaki, Gang Niu, Masashi Sugiyama
Non-Gaussian component analysis (NGCA) is aimed at identifying a linear subspace such that the projected data follows a non-Gaussian distribution. In this paper, we propose a novel…
Direct Estimation of the Derivative of Quadratic Mutual Information with Application in Supervised Dimension Reduction
Voot Tangkaratt, Hiroaki Sasaki, Masashi Sugiyama
A typical goal of supervised dimension reduction is to find a low-dimensional subspace of the input space such that the projected input variables preserve maximal information about…
Regularized Multi-Task Learning for Multi-Dimensional Log-Density Gradient Estimation
Ikko Yamane, Hiroaki Sasaki, Masashi Sugiyama
Log-density gradient estimation is a fundamental statistical problem and possesses various practical applications such as clustering and measuring non-Gaussianity. A naive two-step…
Simultaneous Estimation of Non-Gaussian Components and their Correlation Structure
Hiroaki Sasaki, Michael U. Gutmann, Hayaru Shouno +1
The statistical dependencies which independent component analysis (ICA) cannot remove often provide rich information beyond the linear independent components. It would thus be very…
Direct Density-Derivative Estimation and Its Application in KL-Divergence Approximation
Hiroaki Sasaki, Yung-Kyun Noh, Masashi Sugiyama
Estimation of density derivatives is a versatile tool in statistical data analysis. A naive approach is to first estimate the density and then compute its derivative. However, such…
Clustering via Mode Seeking by Direct Estimation of the Gradient of a Log-Density
Hiroaki Sasaki, Aapo Hyvärinen, Masashi Sugiyama
Mean shift clustering finds the modes of the data probability density by identifying the zero points of the density gradient. Since it does not require to fix the number of cluster…