[zh] cs-229-unsupervised-learning #48
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tigerneil
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Awesome work @tigerneil! |
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OK👌 |
spin6lock
reviewed
Oct 10, 2018
spin6lock
reviewed
Oct 10, 2018
spin6lock
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Oct 14, 2018
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| 32. **Eigenvalue, eigenvector ― Given a matrix A∈Rn×n, λ is said to be an eigenvalue of A if there exists a vector z∈Rn∖{0}, called eigenvector, such that we have:** | ||
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| ⟶ 特征值，特征向量 - 给定矩阵 A∈Rn×n，λ 被称为 A 的一个特征值当存在一个称为特征向量的向量 z∈Rn∖{0}，使得： |
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给定矩阵A∈Rn×n，如果存在称为本征向量的向量z∈Rn∖{0}，则称λ为A的特征值，这样我们就得到：
spin6lock
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Oct 14, 2018
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| 36. **Algorithm ― The Principal Component Analysis (PCA) procedure is a dimension reduction technique that projects the data on k dimensions by maximizing the variance of the data as follows:** | ||
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| ⟶ 算法 - 主成分分析（PCA）过程就是一个降维技巧，通过最大化数据的方差而将数据投影到 k 维上： |
spin6lock
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Oct 14, 2018
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| 46. **The goal is to find the unmixing matrix W=A−1.** | ||
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| ⟶ 目标是要找到去混合矩阵 W=A−1。 |
spin6lock
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Oct 14, 2018
yiyang-yu
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Jan 8, 2019
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| 35. **Remark: the eigenvector associated with the largest eigenvalue is called principal eigenvector of matrix A.** | ||
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| ⟶ 注：关联于最大的特征值的特征向量被称为矩阵 A 的主特征向量。 |
yiyang-yu
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Jan 8, 2019
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| 38. **Step 2: Compute Σ=1mm∑i=1x(i)x(i)T∈Rn×n, which is symmetric with real eigenvalues.** | ||
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| ⟶ 步骤 2：计算 Σ=1mm∑i=1x(i)x(i)T∈Rn×n，其为有实特征值的对称阵 |
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步骤 2：计算 Σ=1mm∑i=1x(i)x(i)T∈Rn×n，它是对称阵，特征值是实数。
shervinea
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