CODEWITHSUNDEEP
pythonnumpymachine-learningtensorfeature-extraction
Josip Rođak
Josip Rođak

Reputation: 5

Implementing the Discriminant Algorithm for Reduced Multidimensional Features in Python

I need to implement the Discriminant Algorithm for Reduced Multidimensional Features proposed in the paper https://www.jstage.jst.go.jp/article/nolta/1/1/1_1_37/_pdf/-char/en as Algorithm 2. I am using Tensorly, Scipy and Numpy for the implementation of this algorithm. I have tried implementing the algorithm, but it fails to converge. Additionaly, the projection matrix psi (and subsequently the F_matrix) contain large Complex values.

def algoritam2(G, I, F, max_iter,tol):
    """
    Discriminant Algorithm for Reduced Multidimensional Features

    Parameters:
    G : numpy.ndarray
        Core feature tensor of dimension J1 x J2 x ... x JN x K

    I : numpy.ndarray
        Target labels for core tensor G (K features in C classes)

    F : int
        Number of discriminatory features we want

    max_iter: int
        Maximum number of iterations when maximizing the trace ratio
    
    tol: float
        Tolerance for dynamic criteria for convergence


    Return values:
    Psi : numpy.ndarray
        Discrimination projection matrix, dim L x F

    F_matrix : numpy.ndarray
        Matrix of discriminatory features, dim F x K

    note: L = J1 * J2 * ... * JN
    """
    K = G.shape[-1] 
    N = G.ndim - 1  
    klase = np.unique(I)

    G_mean = np.mean(G, axis=-1)

    G_c_mean=np.zeros(G.shape[:-1]+(len(klase),))
    G_tilda=np.zeros(G.shape)

    for c in klase:
        ind=np.where(I==c)[0]
        G_c = G[..., ind]  # Uzori iz klase c
        K_c = G_c.shape[-1]   # Broj uzoraka u klasi c
        G_c_mean[...,c] = np.mean(G_c, axis=-1)  # Prosjek za klasu c

        for i in ind:
            G_tilda[...,i]=G[...,i]-G_c_mean[...,c]  #Centralizacija klase c
        G_c_mean[...,c]=np.sqrt(K_c)*(G_c_mean[...,c]-G_mean)

    S_w = tl.base.unfold(G_tilda,N).T @ tl.base.unfold(G_tilda,N)
    S_b = tl.base.unfold(G_c_mean,N).T @ tl.base.unfold(G_c_mean,N)

    delta, psi = eigs(S_b,k=F,M=S_w,which='LM')

    br_iter=0
    uvjet=1

    while br_iter<max_iter and uvjet:
        psi_stari=psi
        br_iter+=1
        
        fi=np.trace(psi.T @ S_b @ psi)/np.trace(psi.T @ S_w @ psi) 
        delta, psi = eigs(S_b-fi*S_w,k=F,which='LM')
        delta, psi = eigs([email protected]@S_w@[email protected],k=F,which='LM')
        uvjet=(np.linalg.norm(psi-psi_stari)>=tol)

    F_matrix = psi.T @ tl.unfold(G, N).T
    return psi, F_matrix

Can you help me find the bug?

Upvotes: -2

Views: 30

Answers (0)

Related Questions