パラメータ制約付き特異モデルの統計的学習理論

• 資料種別: 博士論文
• タイトル: パラメータ制約付き特異モデルの統計的学習理論
• 著者・編者: 林, 直輝; Hayashi, Naoki
• 著者標目: 林, 直輝; Hayashi, Naoki
• 出版年月日等: 2021-06
• 出版年（W3CDTF）: 2021-06
• 並列タイトル等: Statistical Learning Theory of Parameter-Restricted Singular Models
• 授与機関名: 東京工業大学
• 授与年月日: 2021-06-30
• 報告番号: 甲第12028号
• 学位: 博士(理学)
• 本文の言語コード: eng
• 件名標目: ベイズ推論; Bayesian inference; 統計的学習理論; statistical learning theory; 特異モデル; singular model; 実対数閾値; real log canonical threshold; 汎化誤差; generalization error; パラメータ制約; parameter restriction; 非負値行列分解; non-negative matrix factorization; 潜在ディリクレ配分; latent Dirichlet allocation
• 対象利用者: 一般
• 一般注記: Statistical models used in machine learning are called learning machines. It is well-known that learning machines are widely applied to predict unknown events and discover knowledge by computers in many fields. Indeed, machine learning has grown over the last several decades. They are used for statistical learning/inference and usually have hierarchical structures. These structures are effective for generalizing to the real world. Statistical learning theory is a theory to clarify the generalization performances of learning machines.Singular learning theory is a mathematical foundation for statistical inference using singular models. Typical hierarchical models, such as neural networks, tree and forest model, mixture model, matrix factorization, and topic model, are statistically singular since a map from a parameter to a probability density function is not one-to-one. Clarifying generalization behaviors in singular models is an important problem to estimate sufficient sample sizes, design models, and tune hyperparameters. However, conventional statistics theory cannot be applied to these models because their likelihoods cannot be approximated by any normal distribution.Singular learning theory provides a general view for this problem; birational invariants of an analytic set (a.k.a. algebraic variety) determine the generalization error. That is defined by zero of a Kullback-Leibler (KL) divergence between the data-generating distribution and the model. Algebraic structures of statistical models are essential in singular learning theory; thus, it can be interpreted as an intersection between algebraic statistics and statistical learning theory.One of such invariants is a real log canonical threshold (RLCT). An RLCT is a negative-maximum pole of a zeta function defined by an integral of a KL divergence. Determining an RLCT of a concrete model is performed by resolution of singularities. In fact, algebraic statisticians and machine learning researchers have derived the exact values or upper bounds of the RLCTs for several singular models. The theoretical value of the RLCT is effective in statistical model selection such as sBIC proposed by Drton and Plummer. Besides, Nagata proposed a tuning method using RLCTs for exchange Monte Carlo.On the other hand, from the practical point of view, the parameter region of the model is often restricted to improve interpretability. Non-negative matrix factorization (NMF) and latent Dirichlet allocation (LDA) are well-known examples of parameter-restricted singular models.In general, such constraints make the generalization error changed. However, for each singular model and condition, the quantitative effect of those constraints has not yet been clarified because the singularities in the above analytic set are also changed by the restriction to the parameter region.In this dissertation, as a foundation to establish a singular learning theory of parameter-restricted statistical models,we theoretically study the asymptotic behavior of the Bayesian generalization error in NMF and LDA. NMF and LDA are two typical singular models whose parameter regions are constrained.In NMF, we derive an upper bound of the RLCT and a lower bound of the variational approximation error.In LDA, we prove that its RLCT is equal to that of matrix factorization with simplex restrictionand clarify the exact asymptotic form of the generalization error, i.e. we determine the exact value of the RLCT of LDA.These results provide quantitative differences of generalization errors from matrix factorization whose parameter space is not restricted.; identifier:oai:t2r2.star.titech.ac.jp:50574456
• 記録形式（IMT）: application/pdf
• 一次資料へのリンクURL: fulltext; http://t2r2.star.titech.ac.jp/rrws/file/CTT100848643/ATD100000413/thesis.pdf
• オンライン閲覧公開範囲: インターネット公開
• 連携機関・データベース: 国立情報学研究所 : 学術機関リポジトリデータベース（IRDB）（機関リポジトリ）; https://irdb.nii.ac.jp
• 提供元機関・データベース: 東京工業大学 : 東京工業大学リサーチリポジトリ