Crashes when a feature variance is 0

[zyxue]

It would be more desirable if it can skip features with zero variance instead of crashing.

I have written some code to do the check myself, but it feels very inefficient. Suggestion on improvement is welcome

        let mat = new Matrix(input).transpose();
        let mat2 = [];
        mat.forEach((vec, idx) => {
            let mean = this.mean(vec);
            let variance = this.variance(vec, mean);
            if (variance > 1e-7) {
                let svec = this.standardize(vec, mean, variance);
                mat2.push(svec);
            } else {
                // consider 0 variance
            }
        })
        mat2 = new Matrix(mat2);
        mat2 = mat2.transpose();

        // scaled myself to avoid 0-division (caused by 0-variance) problem;
        let pca = new Stat.PCA(mat2, {mean: false, scale: false});

[loretoparisi]

any news?

[targos]

I'm working on the project again. I'll have a look at this.

[targos]

What should we do when the standard deviation is 0 ? Subtract the mean without scaling? I'm going to check what R does.

[targos]

R also throws an error if the dataset cannot be scaled because of a constant column. Is there precedent in other libraries / languages? What do they do in this case?

[zyxue]

I think if there are multiple features, the one with no variance should just be ignored as it doesn't contribute anything to PCA.