29 citations · 79 across the 22 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…
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…
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…
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…