Truncated SVD
Truncated SVD Sep 9, 2023 PCA Principal Component Analysis (PCA) is the subject of a previous post of mine, so I will only summarize it here. Data reduction via PCA is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data. Without getting into the details, this involves an eigen-decomposition of the covariance matrix.
- ▪Truncated SVD Sep 9, 2023 PCA Principal Component Analysis (PCA) is the subject of a previous post of mine, so I will only summarize it here.
- ▪Data reduction via PCA is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data.
- ▪Without getting into the details, this involves an eigen-decomposition of the covariance matrix.
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| Original publisher | Brashandplucky |
| Canonical URL | https://brashandplucky.com/2023/09/09/truncated-svd.html |
| Publication time | Mon, 14 Sep 2026 16:00:33 +0000 |
| Retrieval time | 2026-09-14T16:46:51.498Z |
| Last seen | 2026-09-14T16:46:51.498Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | None |
| Cluster logic | Not yet clustered, or no peer story found in the clustering window. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Truncated SVD Sep 9, 2023 PCA Principal Component Analysis (PCA) is the subject of a previous post of mine, so I will only summarize it here. Data reduction via PCA is accomplished by linearly transforming the data into a new coordinate system where (most of) the variation in the data can be described with fewer dimensions than the initial data. Without getting into the details, this involves an eigen-decomposition of the covariance matrix. SVD Singular Value Decomposition (SVD) is a matrix factorization technique that factors a real matrix M into three matrices U, Σ, and V such that M=U*Σ*V^T. If M is mxn, then U is mxm, Σ is mxn and V is nxn. Both U and V are orthonormal, and Σ is rectangular-diagonal with non-negative coefficients.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Brashandplucky.
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