29 citations · 79 across the 22 of their papers we have counts for
22 papers
Adaptive Topological Feature via Persistent Homology: Filtration Learning for Point Clouds
Naoki Nishikawa, Yuichi Ike, Kenji Yamanishi
Machine learning for point clouds has been attracting much attention, with many applications in various fields, such as shape recognition and material science. For enhancing the ac…
Tight and fast generalization error bound of graph embedding in metric space
Atsushi Suzuki, Atsushi Nitanda, Taiji Suzuki +3
Recent studies have experimentally shown that we can achieve in non-Euclidean metric space effective and efficient graph embedding, which aims to obtain the vertices' representatio…
Detecting Signs of Model Change with Continuous Model Selection Based on Descriptive Dimensionality
Kenji Yamanishi, So Hirai
We address the issue of detecting changes of models that lie behind a data stream. The model refers to an integer-valued structural information such as the number of free parameter…
Generalization Error Bound for Hyperbolic Ordinal Embedding
Atsushi Suzuki, Atsushi Nitanda, Jing Wang +3
Hyperbolic ordinal embedding (HOE) represents entities as points in hyperbolic space so that they agree as well as possible with given constraints in the form of entity i is more s…
Detecting Hierarchical Changes in Latent Variable Models
Shintaro Fukushima, Kenji Yamanishi
This paper addresses the issue of detecting hierarchical changes in latent variable models (HCDL) from data streams. There are three different levels of changes for latent variable…
Word2vec Skip-gram Dimensionality Selection via Sequential Normalized Maximum Likelihood
Pham Thuc Hung, Kenji Yamanishi
In this paper, we propose a novel information criteria-based approach to select the dimensionality of the word2vec Skip-gram (SG). From the perspective of the probability theory, S…
A Novel Global Spatial Attention Mechanism in Convolutional Neural Network for Medical Image Classification
Linchuan Xu, Jun Huang, Atsushi Nitanda +2
Spatial attention has been introduced to convolutional neural networks (CNNs) for improving both their performance and interpretability in visual tasks including image classificati…
Mixture Complexity and Its Application to Gradual Clustering Change Detection
Shunki Kyoya, Kenji Yamanishi
In model-based clustering using finite mixture models, it is a significant challenge to determine the number of clusters (cluster size). It used to be equal to the number of mixtur…
Detecting Change Signs with Differential MDL Change Statistics for COVID-19 Pandemic Analysis
Kenji Yamanishi, Linchuan Xu, Ryo Yuki +2
We are concerned with the issue of detecting changes and their signs from a data stream. For example, when given time series of COVID-19 cases in a region, we may raise early warni…
Online Robust and Adaptive Learning from Data Streams
Shintaro Fukushima, Atsushi Nitanda, Kenji Yamanishi
In online learning from non-stationary data streams, it is necessary to learn robustly to outliers and to adapt quickly to changes in the underlying data generating mechanism. In t…
Descriptive Dimensionality and Its Characterization of MDL-based Learning and Change Detection
Kenji Yamanishi
This paper introduces a new notion of dimensionality of probabilistic models from an information-theoretic view point. We call it the "descriptive dimension"(Ddim). We show that Dd…
Long-tailed distributions of inter-event times as mixtures of exponential distributions
Makoto Okada, Kenji Yamanishi, Naoki Masuda
Inter-event times of various human behavior are apparently non-Poissonian and obey long-tailed distributions as opposed to exponential distributions, which correspond to Poisson pr…
Adaptive Minimax Regret against Smooth Logarithmic Losses over High-Dimensional -Balls via Envelope Complexity
Kohei Miyaguchi, Kenji Yamanishi
We develop a new theoretical framework, the \emph{envelope complexity}, to analyze the minimax regret with logarithmic loss functions and derive a Bayesian predictor that adaptivel…
Stable Geodesic Update on Hyperbolic Space and its Application to Poincare Embeddings
Yosuke Enokida, Atsushi Suzuki, Kenji Yamanishi
A hyperbolic space has been shown to be more capable of modeling complex networks than a Euclidean space. This paper proposes an explicit update rule along geodesics in a hyperboli…
High-dimensional Penalty Selection via Minimum Description Length Principle
Kohei Miyaguchi, Kenji Yamanishi
We tackle the problem of penalty selection of regularization on the basis of the minimum description length (MDL) principle. In particular, we consider that the design space of the…
Exact Calculation of Normalized Maximum Likelihood Code Length Using Fourier Analysis
Atsushi Suzuki, Kenji Yamanishi
The normalized maximum likelihood code length has been widely used in model selection, and its favorable properties, such as its consistency and the upper bound of its statistical…
Grafting for Combinatorial Boolean Model using Frequent Itemset Mining
Taito Lee, Shin Matsushima, Kenji Yamanishi
This paper introduces the combinatorial Boolean model (CBM), which is defined as the class of linear combinations of conjunctions of Boolean attributes. This paper addresses the is…
Upper Bound on Normalized Maximum Likelihood Codes for Gaussian Mixture Models
So Hirai, Kenji Yamanishi
This paper shows that the normalized maximum likelihood~(NML) code-length calculated in [1] is an upper bound on the NML code-length strictly calculated for the Gaussian Mixture Mo…
Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors
Motohide Higaki, Kai Morino, Hiroshi Murata +2
This study addresses the issue of predicting the glaucomatous visual field loss from patient disease datasets. Our goal is to accurately predict the progress of the disease in indi…
Normalized Maximum Likelihood Coding for Exponential Family with Its Applications to Optimal Clustering
So Hirai, Kenji Yamanishi
We are concerned with the issue of how to calculate the normalized maximum likelihood (NML) code-length. There is a problem that the normalization term of the NML code-length may d…
Discovering Emerging Topics in Social Streams via Link Anomaly Detection
Toshimitsu Takahashi, Ryota Tomioka, Kenji Yamanishi
Detection of emerging topics are now receiving renewed interest motivated by the rapid growth of social networks. Conventional term-frequency-based approaches may not be appropriat…
Document Classification Using a Finite Mixture Model
Hang Li, Kenji Yamanishi
We propose a new method of classifying documents into categories. The simple method of conducting hypothesis testing over word-based distributions in categories suffers from the da…