1. PCAではなく自己符号器（Autoencoder）を使うとき 非線形構造を捉えたいとき PCAは線形変換による次元削減で、データ分布が非線形多様体に沿っている場合は表現力が不足。 Autoencoderは非線形活性化関数を用いるため、複雑な分布や非線形関係を学習できる。

以下では代表的な次元削減手法10選を「名前」「主な特徴」「活用シーン」「向いているデータ」の観点でまとめました。まずポイントを簡単に整理すると、 特徴：データの共分散行列の固有ベクトルを用い、分散が最大となる方向に線形射影する最も基本 ...

Dr. James McCaffrey of Microsoft Research presents a full-code, step-by-step tutorial on creating an approximation of a dataset that has fewer columns. Imagine that you have a dataset that has many ...

pca_experiment.ipynb experiments with PCA in generall. pca_dimreduct.ipynb fits a PCA model (on healthy data only) which is saved and later re-used as a dimensionality reduction method before the ...

The high dimensionality and complex structure of DNA microarray data pose significant challenges for accurate cancer detection, as redundant and irrelevant features may lead to overfitting and reduced ...

This repository contains the implementation of Exercise 5 for the Computer Vision course at Computer Engineering and Informatics Department at University of Patras, focusing on dimensionality ...

Abstract: The high dimensionality and complex structure of DNA microarray data pose significant challenges for accurate cancer detection, as redundant and irrelevant features may lead to overfitting ...