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I'm trying to obtain an estimator $f(x)=y$ where $x \in \mathbb{R}^{D_1}$ and $y \in \mathbb{R}^{D_2}$, both are column vectors. So my training set $X$ and $Y$ are data matrices of size $D_1 \times N$ and $D_2 \times N$, respectively, and I want to learn $\beta$ that gives $\beta x \sim y$ in a least-squares fashion. I was doing this in MATLAB simply by beta_hat = Y * pinv(X); and it seems like working without a problem. Though I want to ask, is this correct?

I'm trying to obtain an estimator $f(x)=y$ where $x \in \mathbb{R}^{D_1}$ and $y \in \mathbb{R}^{D_2}$, both are column vectors. So my training set $X$ and $Y$ are data matrices of size $D_1 \times N$ and $D_2 \times N$, respectively, where $N$ is the number of samples, and $D$'s are the input (feature) and output dimensions.

So I want to learn $\beta$ that gives $\beta x \sim y$ in a least-squares fashion. I was doing this in MATLAB simply by beta_hat = Y * pinv(X); and it seems like working without a problem. Though I want to ask, is this correct?

Edit 2

After @Matthew Drury's suggestion, I replaced the line C = Y * inv(X'*X)*X'; to C = linsolve(X',Y')';

But now I'm getting this error:

Warning: Rank deficient, rank = 17, tol =  2.729816e-12.

Is this normal?

I'm trying to obtain an estimator $f(x)=y$ where $x \in \mathbb{R}^{D_1}$ and $y \in \mathbb{R}^{D_2}$, both are column vectors. So my training set $X$ and $Y$ are data matrices of size $D_1 \times N$ and $D_2 \times N$, respectively, and I want to learn $\beta$ that gives $\beta x \sim y$ in a least-squares fashion. I was doing this in MATLAB simply by beta_hat = Y * pinv(X); and it seems like working without a problem. Though I want to ask, is this correct?

My question:

Now I want to implement this without pinv because I want to add regularization to it, so I came up with this solution (this is without regularization) : $\hat \beta = Y (X^TX)^{-1}X^T$ is this correct? It also works but MATLAB complains about this :

Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  2.565271e-20.

And even crashes sometimes. So I think I'm making a mistake somewhere, but where?

Thanks in advance,

Edit

Here is what my MATLAB code looks like : (I know there are non-initialized variables like N, but I just cropped them out, they are working as expected) :

Ntr = round(N * 0.7); % Assign first 70% of the samples as training set
Trains = [1:Ntr]; Tests = [Ntr+1:N];
XData = zeros(FeatureSize, N);
YData = zeros(OutputSize, N);

for n=1:N
    % Collect the independent data (into the columns of X)
    XData(:,n) = getFeature(sample(n));
    % Collect output variable for Train samples : 
    if find(Trains==n)
        YData(:,n) = getLabel(sample(n));
    end
end % for each sample

% Learn model: 
if strcmp(options.Regression, 'ordinary')
    C = YData(:,Trains) * pinv(XData(:,Trains));
elseif strcmp(options.Regression, 'ordinary_myImplementation')
    X = XData(:,Trains);
    Y = YData(:,Trains);
    C = Y * inv(X'*X)*X'; % this is where the error happens. Isn't this the same with pinv(X) ?
elseif strcmp(options.Regression, 'ridge')
    X = XData(:,Trains);
    Y = YData(:,Trains);
    C = Y * inv(X'*X + alpha*eye(Ntr,Ntr)) * X';
else, error('Unknown regression type');    end

% Apply model on Test samples : 
YData(:,Tests) = C * XData(:,Tests);

I'm trying to obtain an estimator $f(x)=y$ where $x \in \mathbb{R}^{D_1}$ and $y \in \mathbb{R}^{D_2}$, both are column vectors. So my training set $X$ and $Y$ are data matrices of size $D_1 \times N$ and $D_2 \times N$, respectively, and I want to learn $\beta$ that gives $\beta x \sim y$ in a least-squares fashion. I was doing this in MATLAB simply by beta_hat = Y * pinv(X); and it seems like working without a problem. Though I want to ask, is this correct?

My question:

Now I want to implement this without pinv because I want to add regularization to it, so I came up with this solution (this is without regularization) : $\hat \beta = Y (X^TX)^{-1}X^T$ is this correct? It also works but MATLAB complains about this :

Warning: Matrix is close to singular or badly scaled. Results may be inaccurate. RCOND =  2.565271e-20.

And even crashes sometimes. So I think I'm making a mistake somewhere, but where?

Thanks in advance,

Edit

Here is what my MATLAB code looks like : (I know there are non-initialized variables like N, but I just cropped them out, they are working as expected) :

Ntr = round(N * 0.7); % Assign first 70% of the samples as training set
Trains = [1:Ntr]; Tests = [Ntr+1:N];
XData = zeros(FeatureSize, N);
YData = zeros(OutputSize, N);

for n=1:N
    % Collect the independent data (into the columns of X)
    XData(:,n) = getFeature(sample(n));
    % Collect output variable for Train samples : 
    if find(Trains==n)
        YData(:,n) = getLabel(sample(n));
    end
end % for each sample

% Learn model: 
if strcmp(RegressionType, 'ordinary')
    C = YData(:,Trains) * pinv(XData(:,Trains));
elseif strcmp(RegressionType, 'ordinary_myImplementation')
    X = XData(:,Trains);
    Y = YData(:,Trains);
    C = Y * inv(X'*X)*X'; % this is where the error happens. Isn't this the same with pinv(X) ?
elseif strcmp(RegressionType, 'ridge')
    X = XData(:,Trains);
    Y = YData(:,Trains);
    C = Y * inv(X'*X + alpha*eye(Ntr,Ntr)) * X';
else, error('Unknown regression type');    end

% Apply model on Test samples : 
YData(:,Tests) = C * XData(:,Tests);